OPTICAL IMAGE SCANNERS AND CHARACTER
RECOGNITION DEVICES:
A SURVEY AND NEW TAXONOMY
Amar Gupta
Sanjay Hazarika
Maher Kallel
Pankaj Srivastava
Working Paper #3081-89
Massachusetts Institute of Technology
Cambridge, MA
02139
ABSTRACT
Image scanning and character recognition technologies have matured
to the point where these technologies deserve serious consideration
for significant improvements in a diverse range of traditionally
paper-oriented applications, in areas ranging from banking and
insurance to engineering and manufacturing.
Because of the rapid
evolution of various underlying technologies, existing techniques
for classifying and evaluating alternative concepts and products
have become largely irrelevant.
A new taxonomy for classifying
image scanners and optical recognition devices is presented in this
paper.
This taxonomy is based on the characteristics of the input
material, rather than on speed, technology or application domain.
2
1.
INTRODUCTION
The concept of automated transfer of information from paper
documents to computer-accessible media dates back to 1954 when
the first Optical Character Recognition (OCR) device was
introduced by Intelligent Machines Research Corporation [1].
By
1970, approximately 1000 readers were in use and the volume of
sales had grown to one hundred million dollars per annum [3].
In
spite of these early developments, through the seventies and
early eighties scanning technology was utilized only in highly
specialized applications.
The lack of popularity of automated reading systems stemmed
from the fact that commercially available systems were unable to
handle documents as prepared for human use.
The constraints
placed by such systems served as barriers, severely limiting
their applicability.
In 1982, Ullmann [2] observed:
"A more plausible view is that in the area of character
recognition some vital computational principles have not yet
been discovered or at least have not been fully mastered.
If this view is correct, then research into the basic
principles is still needed in this area."
However, ongoing research in the area of automated
reading (conducted as part of the wider field of pattern
recognition) is leading to new products, and the market for
scanning devices has expanded dramatically.
Concomitantly,
there has been tremendous growth in the market for third-
llI
3
party scanning software, as well as software and peripherals
for specialized tasks such as handwriting recognition and
data indexing, storage and retrieval [3].
This paper analyzes the technology used in the image
scanning and character recognition industry and presents a
taxonomy for evaluating the capabilities of current systems.
Although the main focus is on optical scanning and character
recognition technologies, an attempt is made to identify
auxiliary technologies and products as well.
In addition,
the paper discusses future trends and characteristics of
systems which are likely to become available in the next ten
years.
Inclusive of this introductory section, there are nine
sections in the paper.
Section Two presents a discussion of
the principal recognition approaches and technologies.
The
third and fourth sections classify the scanning industry by
market segment and function respectively.
Section Five
discusses ancillary technologies such as scanning software,
data storage, indexing and retrieval products and
handwriting recognition software.
In Section Six, future
trends in products and technologies are discussed.
In
Section Seven, a new taxonomy is developed for evaluating
the capabilities of current scanning systems.
Section Eight
describes a set of representative scanners and applies the
4
taxonomy to judge their performance.
Finally, the
conclusions of the paper are presented in Section Nine.
III
2.
RECOGNITION TECHNIOUES
The recognition of text, the scanning of images, and
the raster to vector conversion of technical drawings have
usually been considered independently of each other.
The
technologies corresponding to these three areas must be
integrated in order to accurately scan and process complex
technical documentation.
In the case of most non -
technical applications, only the first two areas need to be
considered.
A framework that allows recognition of both
text and images is presented in this section.
The three major stages in the processing of a document
are preprocessing, recognition, and post-processing.
These
are discussed in the following subsections.
2.1
Preprocessing
Preprocessing is the conversion of the optical image of
characters, pictures, and graphs from the document into an
analog or digital form that can be analyzed by the
recognition unit.
This preparation of the document for
analysis consists of two parts: image analysis and filtering
[4].
The significance of these parts is described in the
following paragraphs.
(a) Image Analysis -
The first stage of image analysis is scanning [5,9,25].
Scanning provides a raster image of the document with
sufficient spatial resolution and gray scale level for
subsequent processing.
In the case of a picture or a graph,
the issue of gray scale level is more important that in the
case of text.
For text, this phase consists of locating
character images.
With the exception of high end scanners,
which employ contextual analysis as well, reading machines
are character-oriented.
Each character is treated as a
unique event and is recognized independently of other
characters.
This implies that the document must first be
segmented into distinct characters, and then the identity of
each character recognized separately.
The process begins with the optical system taking a
raster image of the area that is supposed to enclose the
character.
Alternatively, the raster image representing the
character is "cut out" of the image of the document.
In
either case, the image is transmitted sequentially to a
single-character recognition subsystem.
If the image, or
the information on the features of the character
constituting the image, possesses characteristics which are
significantly different from the characteristics maintained
by the character recognition subsystem, then the particular
area is deemed to be either an unrecognized character or
ill
noise.
Depending on the system, the output is
a flag or a blank.
expressed as
In some systems, any graphic or
character input which is outside the size limitations is not
flagged but skipped.
The formatted output, in such a case,
contains a blank zone corresponding to the input of the
improper size.
(b) Filtering
After the image is analyzed, filtering takes place.
Filtering minimizes the level of noise.
The latter may be
caused either in the source document or by the optoelectrical transformation mechanism.
The process of
filtering also enhances the image for easier recognition.
One filtering process that eases the extraction of the
features of the character in the recognition phase was
proposed independently by Lashas [5] and Baird, et al. [6].
They presented two OCR readers in which black marks
constituting the character are transformed into quantized
strokes.
Their approach is depicted in Figure 1.
Preprocessing:
New Approaches
The preprocessing phase consists of deriving a high
level representation of the contents of the image.
The
scanned document is seen as a set of blocks corresponding to
independent ways of representing information such as text,
line drawings, graphs, tables, and photographs.
t,-
u-~~~
At4
1a
ot-,;
a.
Is tr
A,
t .*
,
..Oa
w,44 f wltv
_#4*1
4I
w"O+RI
"NO&
.PP
- --- *--dB
ALM
CCR-A font numerals and horizontal and vertical
quantized stroke segments
-"
.C
. or
11111
_
=il
r-
i
11111t- d--
CCR-B font numerals and four detected directions of
quantized strokes
FIGURE 1: QUANTIZED STROKES
Lashas, A., et. al. "Optical Recognition Based On
Analog Preprocessing and Automatic Feature
Extraction". Computer Visin, Graphics and
Image Processinq.vol. 32. pp. 191 - 207.
III
understanding of this document involves the following:
(a) Identification of the major blocks of
information.
(b) Identification of the spatial relationships between the
different blocks (for example, the reading order must be
recognized so that the logical connections between different
blocks of text or graphics can be easily derived).
(c) Identification of the layout features such as the number
of columns, margins and justification.
(d) In the case of text, further identification of
headlines, footnotes and other similar characteristics.
Typically, the textual portion is first distinguished
from other information, and then columns of text are
recognized.
Next, these columns are split into lines of
text which are in turn segmented into single character
images.
A reader which is able to accept a free format document
was described by Masuda et al. [7].
Their scheme of area-
segmentation uses projection profiles obtained by projecting
a document image on specific axes.
Each profile shows the
9
structure of the document image from a particular angle.
The projection profile is very sensitive to the direction of
projection.
In order to check for skew normalization, the
image of the document is incrementally rotated and the
horizontal and vertical projection values are noted.
Through an analysis of the intensity of these values and an
examination of different areas, general text and headlines
are separated (Fig. 2).
Another reading system based on
this approach is described by Kida, et al.
8].
Commercial system today require areas to be manually
segmented [2,5,7].
While segmentation methods are fairly
rudimentary in low-end scanners, methods that permit the
operator to define several text and graphics "windows"
within a page are available on high-end products.
These
methods include the use of a mouse or a light pen to define
text, graphic, or numerical zones.
2.2
Recognition
Recognition occurs at the level of characters in most
commercial page readers.
However, high-end scanners are now
complementing character recognition with sophisticated
contextual analysis.
Techniques used for character
recognition and contextual analysis are discussed in the
following paragraphs.
I
I
I
II
__.·._ __
"a
_
_--
_
_~
_w=
__i__
==
_ - ==
nm
_-
~mWmm~
_
-_ dS
-
.r__ --,
Lw
-
.
_ .4mg
-r~·a
mmi iIii
__-J. ii--a
_
-
-
5-
m am
-
__
'" N
~--
cs,~~_
-
3E3
._
-.
_
'~
- ,.mm
¢ws-_mm,^-
, E_* ps
W
Projection Profiles
Text
line areas extraction
FIGURE
Ma suda,
Read,--
and
':
'
? ~
r--n
SEPARATION OF TEXT
-I.
"Approach to a Smart Document
F? Conference on Computer Vision
Recognition. 1985. pp. 550 - 557.
S0
10
Character Recognition
The character recognition problem is essentially one of
defining and encoding a sequence of primitives that can
represent a character as accurately as possible.
The most
common approaches to character recognition are:
(a) template matching, and (b) feature extraction [9].
(a) Template Matching Technique
Among the oldest techniques for character recognition,
template matching involves comparing the bitmap that
constitutes the image of the character with a stored
template [9].
The amounts of overlap between the unknown
shape and the various stored templates are computed, and the
input with the highest degree of overlap is assigned the
label of the template.
The primitive in this case is a very simple function
that assigns one value to a point of the bitmap if it is
black, and another if it is white
5,9,25].
The performance
of different readers using this technique depends on the
decision algorithms.
This method offers high-speed
processing especially for monofont pages.
Even in the case
of documents containing several fonts which are extracted
from a limited set of fonts of a given size, this technique
can be very effective.
Moreover, this method is relatively
immune to the noise generated during the opto-electrical
Ill
11
transformation.
However, this method is less effective in the case o
unknown fonts, multiple fonts and multisize characters.
Further, it imposes constraints on the format of the page in
areas such as the spacing of the letters and the position of
the text.
If these constraints are relaxed, template
matching becomes slow and costly, because of the need to
translate, scale, and rotate input images.
(b) Feature Extraction
In contrast to the template matching technique which
emphasizes the importance of the overall shape, the feature
extraction approach focuses on the detection of specific
components of the character [5].
This approach assumes that
local properties and partial features are sufficient to
define every character.
Feature extraction techniques identify local aspects
such as pronounced angles, junctions and crossing, and
define properties such as slope and inflection points.
In
one method of recognition, a boolean or a numerical function
that characterizes each feature is calculated and then
applied to the given image [5,7].
Another method involves
defining a partial mask that can be systematically
positioned on the pattern [7].
In a more recent strategy,
12
recognition is based on the analysis of the direction and
connective relationships of the strokes of the character
[7].
Structural Analysis Methods
The use of structural analysis methods is a recurrent
theme in the literature
[1,4,5,6,7,10].
These methods
frequently utilized in commercial reading machines.
are
Each
character is defined as a set of topological primitives such
as strokes, segments, holes, and arcs, and these machines
analyze the characteristics of the scanned inputs to detect
such primitives.
Isolated, unbroken characters are first recognized
using a structural description
9].
These descriptions are
independent of the position and the size of the character.
The shape is then parameterized so that the results of the
structural analysis can be compared with a stored list of
shapes.
Next, the shapes are clustered to discriminate
between characters and to classify them.
The power of
structural analysis depends of the number of features of
each character used by
he system.
An example of the
structural analysis -f a character is shown in Figure 3.
I*
II[
C4
]
lm
C
'O
4
Lu.
0
C
0;
Co
+
)
-i
f4n
a
(A
I
oL
K
a-
0z
4n
xI-.
0
m
I)
U,
0
rr
(3
U)
U.
0i
LU
0
0
I)
O
0
0_
LI I
La
(n
13
Character Classification
So far, the discussion has centered on single
characters in a single font.
Most documents contain
multiple characters with considerable variation across
fonts.
A statistical approach is used for dealing with
these variations.
In reading machines equipped to handle
multiple fonts, tests of character recognition are based on
statistical data constituted by a training set and a test
set.
When the term "omnifont" is applied to a reader, the
implication is that the reader has been trained on a large
set of fonts [6].
Feature extraction is generally complemented by
classification. In order to be classified, characters have
to be discriminated from each other.
The classifier is
designed to construct a discriminant function underlying a
complex probability distribution, taking into account the
non-linear variations in the characters [26].
The two essential steps in recognition - feature
extraction and classification - have different optimization
criteria.
It is felt that feature extraction should be
optimized with respect to the reconstruction of the
character, while classification should be optimized with
respect to recognition
4].
Consequently, feature
extraction should not be directed towards recognition
14
through definition of features that minimize the
classification process.
Classifiers are usually adapted to different fonts by
the manufacturer.
However, some machines provide a
classifier which allows for the adaptation of new fonts by
the operator.
An on-line training capability for multifont
recognition can also be provided
4].
This concept of
trainability is similar to that employed in modern speech
recognition systems, which overcome the variability of voice
between speakers by requiring that adaptation be performed
during a training phase prior to regular operation.
The
main disadvantage of trainable systems lies in their slow
training speed, as the training set contains several
thousands of characters.
Limitations of Current Recognition Techniques
Ideally, feature extraction techniques should be able
to generate a set of characters that are independent of font
and size.
However, the wide variety of fonts encountered in
office environments results in a huge number of possible
characteristics.
Furthermore, ambiguities occur due to the
similarity in the features of different characters across
fonts.
For example, the distinction between 1 (one
, I
(capital I) and 1 (el) across and even within fonts is not
obvious.
Moreover, there are a number of characters which
15
cannot be correctly identified without further information
about their size or position.
For example, the character
"O" (capital o) in one size corresponds to the lower case
"o" in a larger size;
further, this character is the same as
0 (zero) in several fonts [11].
The user can control the accuracy of the system by
adjusting the error and reject rates [12,25].
The error
rate is the percentage of the characters in a document that
are incorrectly identified by the system.
The reject rate
is the probability that the system cannot identify a
character at the desired level of confidence (i.e.,
if the
reject rate is 5%, the system will flag 5% of all
characters, on the average, as being unrecognizable).
Clearly, there is a tradeoff between the error rate and the
reject rate - if a lower reject rate is selected, the error
rate will increase and vice versa [12,25].
Consequently, a
reader may possess a very low error rate, but it may flag or
reject every character that does not offer a high
probability of correctness as defined by the decision
algorithm (13].
There is naother aspect to accuracy.
For example, a
reader may recognize the characters in "F16" correctly, but
then flag them as rejects because the word is not in its
built-in dictionary.
Also, the inability of readers to
16
distinguish between ambiguous characters such as "I"
(capital ai),
"1" (el) and "1" may or may not be considered
to be an error.
The error rate of the entire recognition process is
dependent not only on the functioning of the single
character recognition subsystem but also on the level of
preprocessing [12].
For example, different kinds of
segmentation errors may occur, and lines of text may be
missed or misaligned in the case of a document containing
several columns.
Situations involving such missed or
misaligned lines can be minimized by preprocessing
(described in Section 2.1).
With respect to performance evaluation, character
recognition is sometimes deemed to be a branch of empirical
statistics [4].
There is no reliable way of modelling the
accuracy of a reading machine except by comparison with a
standard set of norms.
The impracticability of statistical
modelling is due to the fact that the pattern generating
process and its multivariate statistics are influenced by a
number of barely controllable, application-dependent
parameters.
Recognition techniques are prone to ambiguities, apart
from involving heavy computation.
The sole reliance on the
17
physical features of the characters results in a high rate
of errors and rejects.
To combat this problem, recognition
methods are complemented by contextual and syntactical
analysis aided by customizable lexicons, especially in highend systems [9,11,13].
In the case of low quality documents with different
kinds of noise and broken and touching characters, the error
rate is high.
Contextual analysis becomes a necessary
condition for reducing the rate of errors and rejects in
such situations.
Separation of Merged Characters
The breaking of images into character blocks is based
on the assumption that each character is separated by
horizontal or sloped line of blank spaces.
However, in the
case of tight kerning, inadequate resolution of the scanner,
poor quality of the document, or high brightness threshold,
adjacent characters spread into each other.
Separating
merged characters involves the following three processes:
(a) Discriminating multiple characters from single character
"blobs";
(b) Breaking the blobs into components that are identified
as single character blobs;
(c) Classifying the different characters and deciding
18
whether to accept or reject the classification.
Defining a set of criteria for distinguishing between
adjacent characters is difficult because of the many ways in
which characters merge together and the fact that merged
characters contain misleading strokes.
Separation of
characters is a computationally intensive task that
significantly slows down the overall recognition process
[2,4,8,121.
Separation is usually possible in a limited
number of cases and for pairs of characters only.
Contextual Analysis Techniques
Contextual analysis is of two types: layout context
analysis and linguistic content analysis
9].
The former
covers baseline information on the location of one character
with respect to its neighbors.
For example, it generates
formatting information and is usually language-independent.
Linguistic analysis on the other hand involves the use of
spelling, grammar and punctuation rules.
For example, a
capital letter in the middle of a word (with lower case
neighbors) is not accepted.
Layout context analysis
capabilities are available on several systems available
today.
However, only the high-end systems offer some degree
of linguistic analysis capabilities.
19
Dictionary Lookup Methods
Several contextual methods for word recognition are
based on the analysis or comparison of a word or string of
characters with stored data for type and error correction.
Spelling checkers are commonly used to complement character
recognition devices [13].
Such checkers may be part of the
recognition software, or they may be part of the text
composition software that the operator uses for correcting
the processed document.
In either scenario, when the size
of the dictionary is large, the search time can be very
long.
Furthermore, if the contextual information is not
considered, the scanned word may be incorrectly converted
into some other word (13].
Several high speed correction
methods use the concept of "similarity measures", which are
based on the fact that most errors are due to character
substitution, insertion, or deletion [22].
The similarity
between two words (the correct word and the garbled word) of
equal length is measured using the Hamming distance.
The
Modified Levenstein distance [9] generalizes the similarity
measure for substitution, insertion and deletion.
The
minimization of this distance forms the optimization
criterion.
Tanaka [14] proposed two methods that yield 10 -
35% and 35 - 40% higher correction rates than a typical
dictionary method and reduce computing time by factors of 45
and 50 respectively.
One of these methods is based on the
assumption that different characters can be classified into
III
20
classes or groups that are independent of each other, so
that a character in one class is never misrecognized as a
character in another class.
This categorization helps to
significantly reduce the probability of errors.
Use of Statistical Information
The frequency of occurrence of different characters
varies widely.
For example, the letter "e" has the highest
frequency of appearance in an English document.
Also, the
letter "t" is the most frequent first letter of a word, and
the letter "q" is always followed by a "u."
Further,
several sequences of character combinations are more likely
to appear than others.
The frequency of occurrence of a
character within a given string of text can be efficiently
modeled by a finite-state, discrete-time Markov random
process [22].
The use of statistical distributions of
different combinations of N characters (N-grams) allows the
implementation of error corr
Viterbi algorithm.
tion algorithms such as the
According to Hou
9], this algorithm can
reduce the error rate by one half.
Linguistic Context Analysis
Characters are primitives of strings constrained by
grammatical rules.
These rules define legitimate and non-
legitimate strings of characters.
Based on the recognition
of some words, the class (noun, verb, etc.) to which they
21
belong can be identified along with the applicable syntactic
analysis to identify misspellings, misplacements, and other
syntactic errors.
A string of words or a sentence can be
decomposed using a parsing tree.
There are several
efficient parsing trees for different types of grammatical
structures [9,12,15].
Syntactic analysis assumes that the text is constrained
by one type of grammar.
all cases.
This assumption need not hold in
Several technical documents contain uncommon
words: serial numbers, technical words, and abbreviations.
Such situations require interaction between the technical
operator and the system, or the use of dictionary lookup
methods.
Except in such special situations, linguistic
context analysis techniques are well suited to the task of
identifying and correcting reading errors [15].
Future of Recognition Technology
Conventional techniques for character recognition based
solely on geometric or analytical properties are not
sufficient to accurately process complex documents at high
speed [22,25].
While the use of contextual analysis
improves accuracy, it does not increase processing speed.
To improve speed, i- becomes necessary to simultaneously
analyze the document from several points of view.
For
example, one unit can analyze the image, another can
III
22
recognize characters, and a third can perform contextual
analysis of the text.
The use of a single recognition technique is
insufficient to solve all ambiguities encountered during the
recognition process.
As such, general algorithms must be
complemented with customized rules for special cases, such
as distinguishing letters that are easily confused (e.g.,
"o" and "0" and "6" and "b"), or recognizing characters
printed in pieces (such as "i",
"jn",
and ";").
Ideally, a
single process control unit should collect all the pieces of
information from the subsystems, order them, and then make
appropriate final decisions [4].
Such a strategy
(functionally depicted in Figure 4) allows the recognition
of different parts to be done in parallel.
2.3
Postprocessing
After the text and graphics are recognized, they must
be encoded for transmission to, and processing by, a word
processor, a graphic editor or a desktop publishing program
(5].
In the case of text, the image is most commonly
converted into the ASCII format.
In the case of graphics,
the storage requirements for the document vary greatly,
depending on the extent of compression.
The storage
capacity may pose a major constraint in some cases.
For
example, in systems which scan at 300 dots per inch (dpi),
one single 8.5" x 11" page requires over one megabyte of
Ia c
LU
I-
I
C;
C
,a-S
SW
GS.
*r
LI
C
S_ ,"
z
I
AFq
Iw
op*,r
O
v
CC
LU n CC
mC
,;0,
.2.
_ __S.
E
C
C
S
)
0
m
-0
O3
LI.
0
Co
3c
C
h.
0.
a
0
'I;
a 0
b.
P cc
0 _
U'.S
L
0
ar)
0
S
o
O
IOL
L
_4
IS, \1
i
=
--
0
_)
;.
LU,,
*
S
&6
C
<LU
U)
I-
O
0
o'a
rC
W
0
co
cn
gC
a
t
._J
r-'---'t
!
U
o
c
x
:
-
uJm
_
w
C
K
U0o
P
E
-)1
o
0
Q
23
memory if stored in raw bitmap format.
While the advent of
optical disks with storage capacities in gigabytes tends to
mitigate the storage problem to some extent, it is usually
desirable to use more efficient storage strategies.
24
3.
CLASSIFICATION OF SYSTEMS
For analytical purposes, the scanning industry can be
divided into four segments: (a) Low-end scanners; (b) Highend scanners; (c) Integrated systems; and (d) Software.
The
characteristics of each segment are explained in the
following paragraphs.
(a)
Low-end scanners
These are small scale systems, usually desktop-based
with relatively low sophistication in dealing with page
scanning and image processing applications (1].
They are
dependent on a host computer system for processing and
storage (commonly an IBM personal computer or a Macintosh).
The capabilities of these systems are enhanced by third
party image and text processing software that is generally
executed on the host computer.
Low-end scanners are either automatic feed or flatbed,
and can handle between 20-100 documents per minute (or 20-50
pages per minute).
The recognition technology tends to be
simple - generally the matrix matching technique is used.
Costs are low, ranging from $1,800 to $7,000.
cost averages around $2,000 [3].
The software
These scanners are geared
towards low volumes (10 - 100 pages or documents at a time),
and relatively low accuracy (1 - 10 letters per page misread
II1
25
or flagged for error on the average).
The Scan-Optics Easy-
Reader 1720 and the Datacopy 730 are examples of popular
low-end scanners.
Most of these machines can scan a page or a portion of
a page as an image or as characters of text, depending on
the intent of the user.
In some cases, the user can specify
which zones within the input page contain text and which
zones include graphic images.
Some of the systems use
separate phases for scanning text and images respectively.
Hand held scanners have entered as the lowest end
products in the scanner market
3].
These usually offer
limited page and text reading capabilities without requiring
any additional software.
They are either plugged directly
into the personal computer like a keyboard or linked by a
simple interface (such as a parallel card).
They are
transparent to the host computer, its operating system,
its application software.
and
The volume handled is very low,
because of the need to physically move the scanner with the
hand.
The scanning speed
ranges from about 50 to 200
characters per second, and the number of
limited to 5 or 10.
$1000) [161.
onts recognized is
Their costs are quite low ($200 -
The Caere Corp OCReader is an example of a
widely used hand-held scanner.
26
(b)
High-end scanners
These are large, sophisticated image/page/document
processors with stand-alone or host based capabilities [1).
They offer mature hardware and software devoted to the tasks
of text recognition and image scanning.
These systems
include sophisticated dedicated processors, editing and
correction workstations, as well as customized recognition
software that uses sophisticated techniques like feature
extraction [30).
They support their own storage systems and
can be readily integrated with other common systems.
High-end scanners feature multi-font and multi-size
flexibility to handle multiple fonts and multiple sizes
along with automatic feeding capability that can cope with
high volumes.
Large numbers of documents (1,000-10,000) can
be accurately processed at high speeds (200 or more pages
per minute or 750 or more documents per minute).
Costs
range from $15,000 upwards with the median falling between
$25,000 - $30,000 [16].
Examples of high-end scanners
include the Calera CDP Series and the Kurzweil family of
scanners.
(c)
Integrated Systems
These systems serve as automated image processing
facilities for the office, and are designed to network with
-
III
27
the existing computer and communications facilities.
A
typical system, controlled by a common operating system such
as UNIX, consists of an enhanced office network that
provides multi-media, multi-user, and multi-tasking access
to its diverse resources
171.
An integrated image system offers various data entry
facilities such as optical character recognition devices
(both low and high end), image scanners, keyboard
workstations and facsimile equipment.
The system is usually
managed by a dedicated processor (a mainframe or a
minicomputer), although LAN-based systems are becoming
increasingly popular.
The processor is procured from a
vendor, or the system is configured to operate within the
existing computational facilities.
Data storage is
centralized and is accessible from all the workstations.
Software is executed either on the processor or on a
decentralized basis [17].
Typically, an integrated system will support a variety
of software packages, including "off the shelf" word
processors, database and statistics programs, and other
professional software tools.
Similarly, at the output level
there is support for a variety of printers, monitors, and
facsimile equipment.
28
Several integrated imaging systems are available,
including the Scan Optics ScanEdit 3200, the Wang Integrated
Image Processing System, and the DRS 4000.
IBM has
announced its own system, the Image Plus, which is expected
to be released in 1990.
Figure 5 provides a schematic
representation of a typical integrated imaging system.
This
paper does not offer a detailed treatment of integrated
systems, as they represent an amalgamation of new imaging
technology with conventional computing and communications
power, rather than a new technological innovation.
(d)
Software
Third party software tools offer powerful scanning and
processing capabilities for both images and text.
As
mentioned earlier, they are substantially enhancing the
capabilities of low-end scanners.
In addition, software
packages utilizing new approaches from areas such as
database management, expert systems and neural networks are
being developed for specialized applications [1].
Most
packages are compatible with the popular desktop scanners as
well as with Macintosh and IBM personal computers.
The orientation of this paper precludes a detailed analysis
of software products.
However, Section Seven provides a
brief treatment of currently available products.
III
0n
(00
* 0
44
41
D-4A
a:'
Z c
zo
UA X
X
L <K
0
0e
az
tit
C
0 a
z
Ul
a" a
(jo
co
.
I
I0
o
cc
0
(.
zLU
z
I-
C
L-
I
0.
0
0
P
4II-
-]
0O
LL
0OC
to-0,
o
z
o
CC"
0
0 0
Q
00
a-
c
40
0
0
O
=
I0 0
cc
(L
:I
z
I
0
4c
Ibe
o
3.
29
4.
FUNCTIONAL CLASSIFICATION OF MACHINES
Apart from classifying products by market segment, it
is feasible to classify the image and character recognition
industry by function.
Thus, "reading machines" can be
divided into four major categories: (a) Document Readers;
(b) "Process Automation" Readers; (c) Page Readers; and (d)
Image Readers.
These groups are described in the following
paragraphs.
(a)
Document Readers
This category of readers was the first to be
introduced.
Developed during the sixties and seventies,
these machines are oriented towards transaction processing
applications such as billing and form processing [4,8,251.
In earlier machines, the source document was prepared in a
rigid format using a stylized type font and the character
set was limited to numeric characters plus a few special
symbols and sometimes alphabetic letters.
Contemporary
machines have greater flexibility in font recognition and in
coping with material of poor quality generated by high speed
printers.
The speed of processing is very high, typically
between 400 and 4000 characters per second.
An important
feature is on-line correction with the help of bitmaps of
unrecognized characters.
30
(b)
Process Automation' Readers
The main goal of these readers is to control a
particular physical process
sorting of postal letters.
4,8,9,25], such as automatic
Since the objective is to direct
each piece of mail into the appropriate sorting bin, whether
or not the recognition process results in the correct
decision for every single character is not critical in this
case.
Since a reader of this type is
designed with specific
applications in view, it is not explicitly considered in
this paper.
However, the class of integrated systems
discussed in Section Three above is capable of performing
"process automation" functions.
(c)
Page Readers
These reading machines were originally developed to
handle documents containing normal typewritten fonts and
sizes (4,8,9,25].
Initially intended for use in the
newspaper and publishing industries, these machines were
designed with the capability to read all alphanumeric
characters.
Until the late seventies, devices of this type
were quite restrictive in terms of their requirements for
large margins, constrained spacing, and very high quality of
printing.
Further, they accepted only a small number of
specially designed fonts such as OCR-A and OCR-B.
The
reading speed for a monofont page was several hundred
characters per second and the price of the machine itself
31
was around one hundred thousand dollars per unit.
The above
situation was significantly altered by the introduction of
the Kurzweil Omnifont Reader in 1978.
As its name implies,
this machine was able to read virtually any font.
Since
then, several other machines belonging to this category have
been introduced, and their capabilities have been
substantially enhanced. For example, in many cases the user
can specify the particular zones on the page to be read, and
the format of each zone.
another common feature.
On-line error correction is
In addition, prices have fallen
steeply to under twenty-five thousand per system.
(d)
Image Readers
These machines were originally designed to meet
Computer Aided Design (CAD) needs, and subsequently came
into more generalized use.
They capture items such as
drawings in the form of their mapped image and then
reprocess that image to generate an equivalent vector
graphics file.
The functional classification described above is fast
becoming obsolete.
As the matrix in Figure 6 indicates, one
of the main trends in the scanning industry is the
convergence of separate functions within a single system
[1,3].
One machine can now perform functions that
previously required multiple machines.
An equally important
II'
AL
LU
W
I- U)
ot
2
0
x
I-
z
0
LU
ICo
z
X
X
X
X
U)
Cz
Z w
LU Z
Zi
zZ
o
(0
U.
z
(I
Zr
z
U)
ot
n./
-J~n
Cl)
LU
I...
z
u.
Z
w
o
L
Z
Z
4
(n
Ji
cn
3
X
X
,=,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Zr
Zc0~
I
Z3
z
LU
Ou
O w
Z
o-cn
un 2
E:
00
a
0
i .
mQtu~
LLDZOI--
WtLU
ja
LUW0c
wa
.ee
3U
OZ<(.
32
trend is the growth in the market for ancillary products
such as third party scanning software, storage-end devices
and data indexing and retrieval software
next section explores this trend.
3,17,18].
The
III
33
5.
ANCILLARY PRODUCTS
The rapid growth of the scanning market has been
accompanied by a proliferation of ancillary products.
These
fall into two broad groups:
(a) Low-nd Enhancement Products
This group consists of low-cost products which are
designed to augment the capabilities of low-end scanning
systems.
They interface with the scanner to perform one or
more functions of a high-end system.
Third party scanner
enhancement software and low-end interfacing devices fall
into this group.
(b) Complementary Products
This group consists of products which complement and
support the capabilities of scanning systems.
Storage-end
devices and software for specialized scanning applications
are examples of complementary products.
The following paragraphs briefly describe the trends
and products within each group.
5.1
Low-End Enhancement Products
These products emerged in response to the substantial
costs of high-end systems.
By performing one or more
34
functions of a high-end system, they enable relatively basic
systems to emulate high-end systems at lower costs.
Scanner
enhancement software and peripheral interfacing devices are
typical examples of this phenomenon, and are discussed in
the following paragraphs.
(a)
Third Party Scanner Enhancement Software
These are page recognition packages like OmniPage,
Advantex, OCR ReadRight and SPOT. They perform tasks such as
creating scanning templates, specifying key fields, spelling
and grammar checking, and separating columns, graphics and
text. Ranging in price from $600 to $4000
3],
these
relatively user friendly packages are compatible with most
popular scanners and support the more widely used word
processing, spreadsheet and image compression formats.
Although scanner enhancement programs enable users to
perform functions beyond the capability of low-end systems,
the claims of some vendors that these programs can replace
high-end scanners are only partially true.
In comparison to
high-end systems, such programs are slower and less
versatile in the number of fonts they can recognize.
They
are also less acurate, and are not very good at separating
columns, graphics and text.
However, they represent a
serious alternative for many users who do not need or cannot
afford a high-end system [16]. A good example is the desktop
III
-
35
publishing industry, where user friendliness is a primary
concern.
High-end system vendors have had relatively little
success in accessing this market, but third party packages
used with low-end scanners have been extremely popular.
According to International Data Corporation [18], overall
sales for these products have grown by 215% in the period
between 1986 and mid 1989.
(b)
Low-End Interfacing Devices
This group of scanner ancillaries is the hardware
equivalent of scanner enhancement software.
These products
are usually plug-in boards for personal computers, and they
interface with the host system and its low-end scanner to
perform high-end scanning functions.
Like scanner
enhancement programs, these devices enable users to
recognize text and graphics and "read" them into word
processing and other formats.
Specification of key fields
and template creation are also supported.
from $1000 to $5000
Ranging in price
[16], low-end interfacing devices suffer
from the same drawbacks as their software counterparts, such
as slower speeds and limited recognition of fonts.
5.2
Com1lementary Products
This group of products complement and support the
overall capabilities
f scanning systems.
Storage-end
hardware and data indexing and retrieval software are
36
included in this group. In addition, there are a number of
products which combine scanning technology with technology
from other areas to perform specialized scanning
applications.
One example of this trend is the advent of
neural network based programs for handwritten character
recognition
(a)
191.
Storage-End Devices
Scanning systems require much more storage space than
is available with traditional magnetic storage media [28].
Using an optical disk system with several gigabytes of
storage space is one way of meeting this need.
A survey by
Frost and Sullivan in 1989 [3] found that between 1986 and
1988, sales of optical disk systems grew by over 150%.
These systems consist of one or more optical disk drives
(usually Write Once Read Many or WORM drives) and sometimes
a "jukebox" to perform disk storage, retrieval, loading and
operating tasks [21].
However, these systems are very
expensive and a typical system with two disk drives and a
five platter jukebox costs between $27,000 and $33,000 [18].
In addition, one must pay for sophisticated software
packages which store, index and retrieve data on CD-ROM's.
One alternative to optical disk technology is being
marketed by Scan Optics, Inc.
This is the Image EasyFile
System, which uses 8mm cassette tapes (on up to 28
37
concurrent drives) as the storage medium.
capacity of 2.3 gigabytes.
Each tape has a
As data on cassette tapes have
to be accessed in a serial manner, this system is much
slower than an optical disk system.
However, at $ 17,000
for a 28 drive unit, Image Easyrile is considerably cheaper
than an equivalent optical disk storage system.
A cassette
based system is a viable alternative to an optical disk
system in situations where the access frequency per data
point is low.
(b)
Software for Specialized Applications
An interesting development in the scanning industry has
been the joint application of scanning technology and
technology from other fields to handle specialized scanning
tasks.
A good example is the development of neural network
based scanning software for handwritten character
recognition.
Neural network computer models are based on
models of the functioning of the human brain.
They emulate
thought processes such as learning, and can be taught to
perform complex tasks [19]. The handwriting recognition
application emerged as a result of the use of neural
networks in the wider area of pattern recognition.
A number of packages are currently available, either
off-the-shelf or as part of a customized service that
configures handwriting recognition solutions to customer
38
needs.
NestorWriter of Nestor Inc., Providence, Rhode
Island is an example of the former, and Inscript of
Neurogen, Inc., Brookline, Massachusetts is an example of
The main attraction of these packages is that
the latter.
they can be trained to recognize a much wider range of
handwritten characters than is possible with conventional
scanning software.
Most products in this area are designed for DOS, OS/2
and Macintosh environments.
Data are entered through a
scanner or a digitizing tablet, and recognition speeds range
from 1 to 3 characters per second.
As in the case of
conventional scanning software, there is a tradeoff between
the accuracy rate and the rate of rejection.
The accuracy
rate can be made arbitrarily high if tolerance for error is
low, and vice versa.
The main problem with these packages is that it takes a
very long time to train them.
In addition, they scan at
much slower speeds than conventional systems do.
these problems are resolved,
used.
Until
they are unlikely to be widely
However, they have excellent long term potential, and
should eventually c me
industry.
nto widespread use by the scanning
Short term :rospects are brightest in areas such
as scanning numeriJai
verification.
-cmunts on checks and signature
39
The next section discusses broad trends in the optical image
scanning and character recognition industry.
40
6.
TRENDS AND PROJECTIONS
Rapid advances in hardware and software have created
a major demand for scanning technology in diverse industries
ranging from banking to defense and from engineering to medicine
171.
Instead of focusing on each application
separately, this section focuses on broad trends.
Figure 7
presents a schematic representation of the evolution of the
capabilities of scanning technology.
6.1
Falling Costs
one of the primary reasons for the growing demand for
scanning technology is the fall in the cost of products. As
technology improves, costs will continue to decline.
Figure
8 depicts this trend of falling costs in terms of the system
costs per font.
6.2
Coalescence of Text, Image and Graphics Processing
Graphical information can be stored either as simple
bitmaps or as a set of standard geometric entities.
The
latter option allows for easy editing and storage in a
library of symbols.
Currently available scanners do not
offer these capabilities.
However, developments are taking
place in the area of Computer Aided Design (CAD) for
converting raster images of line drawings into vector
graphics files, which can be easily modified with graphic
C
I
C O
C c
*
tL :
c,..
.5;
I0a).
LU
Q
Bu
a
Im
0
CL
__
~
.
2..
o- o -
C,)
,-a
0
zZ
Z
*
x
as
E;.
_o
O
olo
o
-
-
0
_,
a
0
.:
C
z
LU
s
E.
LU
_ DE
_
0a
It
~~~~~~~~~~~~~~
~
-~~1
O0~~~~~~~~~~~~~~~~~~~~~~~
OCI~~~~~~~
CC
z~~ ~
:s
_ "o
0Ul
- -... C
5
.EEc
'"E
.
a
@0
0O
"O
iv
0a
a
CI-
LU
z
0
0
ao
Wa
Zo
u(
m
-
a
LLU
- aN
Z
C O
O
U0
Z -
0
Z
LL
r
E
*
a
_m
m
-
0
i
_
c
o
o
a-*)
*
a
c
S
C
3
co
U
w
0
c
w
CC
XUl
a
a
1n
-LIE
-
0r
°
-o
rr
~
0
III
41
line editors.
Raster-to-vector conversion systems are now
available from several companies including Houston
Instruments and Autodesk Inc.
However, at present only
approximate solutions are supported.
For example, all
curves are decomposed into line segments.
It is expected
that ideas from the area of raster to vector conversion will
be combined with scanning technologies to enable text,
images, and line graphics to be edited and processed through
a composite package.
6.3
Integration and Networking
one of the most significant trends in the information
processing sector as a whole is the increasing integration
of disparate data processing equipment into a single
composite system (17]. This trend is reflected in the
scanning industry by the emergence of the integrated imaging
system (described in Section Three).
The trend towards integration can be observed in other
areas of the scanning industry.
For example, optical
character recognition capabilities are being increasingly
integrated into facsimile equipment
29].
At the same time,
facsimile capabilities are also becoming available on
document readers such as the Datacopy 730.
42
6.4
Impact of Artificial Intelligence Techniques
Advances in Artificial Intelligence techniques in areas
related to character recognition and image analysis will
lead to faster, more accurate and versatile reading machines
[231.
Semantic analysis and natural language parsing aids
for contextual analysis will improve the process of
identifying letters and words, reducing the error rates, the
reject rates and the need for operator intervention [24].
Eventually, reading machines will be able to handle
virtually all printed material [18,271.
6.5
Use of Secial Purpose Hardware
A highly accurate omnifont reader requires
sophisticated algorithms for vectorization and
classification, separation of merged characters, contextual
analysis, and syntactical analysis.
These algorithms can
benefit from microprogramming and special purpose hardware
which is optimized for specific applications.
Sophisticated
readers such as the Calera CDP 9000 and Kurzweil 5100 use
special purpose hardware.
6.6
Limiting Factors
Although it appears that ongoing research will enhance
the capabilities of scanning systems, several factors
continue to impede a wider acceptance of the technology.
These are as follows:
III
43
(a) The accuracy provided by current systems is still
inadequate for several applications.
The editing of errors
and the presence of unrecognized characters continue to be
major bottlenecks.
The time taken by the operator to
overcome these bottlenecks limits the throughput of the
overall system.
(b)
Wide acceptance of the new technology will occur only
after it becomes possible to automatically handle complex
documents containing both text and graphics [13,17,20].
The
need to hire and train expert operators continues to inhibit
the widespread adoption of scanning systems.
(c)
Broken strings and touching characters in a document
still constitute a major hurdle for even the most
sophisticated machines.
Scanning systems remain highly
sensitive to variables such as paper quality and thickness,
clarity and font.
Major developments in syntactical and
semantic analysis are needed before reading machines realize
their full potential.
The major trends and projections are summarized in Table 1.
1111111
g11
'S~~~~i
I I fI1
Qe
*-
I
I
I
I
is
aiii
i, t1
If
'11
II
j'
a
E
iLX
itI
III
44
7.
A TAXONOMY FOR PERFORMANCE EVALUATION
The conventional criteria for evaluation of a reader
are speed and accuracy.
However, by expressing the speed
simply in characters per second and the accuracy in a single
figure, one is
overlooking the fact that the performance of
a scanning system is heavily dependent on the
characteristics of the input.
For example, it takes longer
to process a document with multiple fonts and multiple
character sizes than it does to process a monofont, monosize
document [9].
The presence of graphics and the formatting
of the document also affects the reading speed.
Further,
the quality of the print is a major factor affecting the
accuracy of the system; broken and touching characters, low
contrast, and skewed text result in high error rates and
reject rates as well as in a significant reduction in the
speed of reading.
The speed and the error rate presented in
the technical documentation supplied by the vendors consider
the characteristics in only one case - usually the perfect
one.
In order to assess the capabilities of a system, one
must consider not only the scanning speed but also factors
such as time spent in editing and correcting, time spent in
training the operators and time spent in training the system
itself where applicable [13].
In the case of a document
45
containing several graphs and multiple columns, the time
spent in editing the document can be significantly greater
than the scanning time [12,13].
Consequently, it is
difficult to obtain an accurate estimate of the overall
speed by simply observing the elapsed time for the scan
operation.
With rapid evolution in the technologies used in the
new generation of products and their broad functionality, it
becomes necessary to use a larger repertoire of evaluation
criteria that gives appropriate weightage to the major
factors that determine the efficiency of the scanning
process, such as: complexity of document; quality of
document; recognition technology used; and man - machine
interface.
Unfortunately, there is no framework that
currently meets this need.
In order to fill this void, a
new taxonomy for the evaluation of scanning systems is
developed in this section.
7.1
Document Complexity
The above facts highlight the need for a measure of
document complexity that takes into account the diversity of
fonts utilized, the sze of characters, and the proportion
of images in the docment.
a five-part document
Since no measure exists,
:mplexity classification scheme is
proposed to facilitate systematic analysis.
This
III
46
classification groups documents into the following classes
(in increasing order of complexity):
(a) Class 1:
Basic Text - Only Documents:
All material is
in a single font, with a single pitch and uniform spacing.
An example of this class is a typewritten document, as shown
in Figure 9 (a).
(b) Class 2:
Single Column Documents with Multiple Fonts
and Mixed Sacing:
This covers text-only documents with
proportional spacing, as well as typeset and laser printed
documents with multiple formats (such as bold or
hyphenated).
A sample Class 2 document is shown in Figure
9 (b).
(c) Class 3:
Single Column Documents with Segregated Text
and Images: Such documents contain all material in a single
column format.
The text is justified or hyphenated and
there are some images.
These images can be easily separated
from the text (separate zones for text and images), as in
the case of the example in Figure 9 (c).
(d) Class 4:
Multicolumn Documents:
two or more columns on a page.
Such documents contain
Although they have mostly
text, there are some images and tabular material.
A printed
page from a newspaper will fall under this category.
A
-2-
Dr. A. Gupta
May/86
- Figure references are not consistent (some with, some without
parentheses).
- The references quoted on page 6 are nowhere indicated in the text,
except for the * on page 5, line 14.
- The figures are of poor quality. Figs. 2 and 3 are unnecessarily
complicated, Fig. 4 lacks one legend in.the ordinate.
All the above could be corrected by a complete rewrite. The idea is
interesting, having a minimum configuration microprocessor display
current, power and maximum demand by a TLU method on current values
obtained safely by a non-invasive current transformer. I would still
question the following:
- Only current ismeasured and the data that are displayed are based
upon, previous, actual current and power measurements, stored inthe
table. As power is still a function of current and voltage (and phase
angle if the load is not purely resistive), the results will be in error
ifthe line voltage changes. Inaddition, unless it is the intention to
use this only on one particular load, the type of load will influence ^
the results, making this system not quite general purpose.
- No mention ismade of the actual hardware used. From the references
I presume it is an Intel machine. The type of ADC (speed, number of
bits) and the method of display (printer, 7-segment displays) are alsonot mentioned.
- Page 3: "for every possible digital value, the current and power are
determined and stored ... ".
In what format? The description on page 4
and the flowcharts of Figs. 2 and 3, leave much to the imagination, unless
the reader knows exactly the configuration of their microcomputer. The
references to the "status", "addresses" and "data" field suggest to me
that some type of single board computer, that has these fields as displays,
was used. Can it be assumed that all readers are familiar with this?
- If I follow the flowchart in Fig. 3 (maximum demand display) correctly,
when a subsequent current value is determined to be less than the previous
one, a search through the complete table is made, using the same, old,
input data before displaying the, same, results. I don't know how much
memory is included in their system, but surely the previous value could be
stored somewhere to avoid this unneeded search.
- Similarly, if the next value is determined to be higher than the previous
one, again a complete table search is made instead of starting at the
address of the last (lower) value.
- In the three phase measurement system, a simple two comparison schedule
should be added to indicate the highest load, rather than leaving this up
to the human operator.
FIGURE-9
(a):
SAMPLE DOCUMENT -
CLASS 1
III
HIGHLIGHTS OF
KNOWLEDGE-BASED INTEGRATED INFORMATION SYSTEMS ENGINEERING
AMAR GUPTA AND STUART E MADNICK
Large organizations must necessarily rely on multiple computer systems for a number of reasons, such as
the increasing size of the organization and the growing reliance on computerized data. In virtually all
cases, dissimilar and incompatible hardware and software systems are operating on a concurrent basis.
While these systems may meet the objectives for which each was designed, their heterogeneity presents a
major obstacle to ready access and assimilation of the information they contain.
The objective of Integrated Information Systems, or Composite Information Systems (CIS), is to mitigate
the problem described above. Such systems must be geared to span applications, functional areas,
organizational boundaries, and geographic separations in order to present a unified picture to the user.
While designing such systems, it is necessary to look at a number of inter-related strategic, technical, and
organizational issues. The goal of the Knowledge-Based Integrated Information Systems Engineering
(KBIISE) effort, highlighted in this report, is to survey the state-of-the-art of methodologies for
addressing these needs and identify areas needing increased research focus.
Strategic issues include motivating cooperation between multiple organizations, each with its own glis
priorities, and security needs. One critical success factor for such cooperation is participantconsensus on
the issue of access to each others' technical and non-technical information. There is an urgent need to
clearly define the domains of shared information, the potential benefit to each group that participates,
and the role and the responsibility of each constituent.
Under technical issues, the evolution of distributed heterogeneous information systems is studied. Being
inherently more complex than conventional databases, such systems require powerful semantics and
update capabilities, as well as sophisticated concurrency control and recovery mechanisms. Both the
semantics and the syntax of individual queries and updates must be mapped across systems. In addition
to physical connectivity issues, it becomes essential to develop new techniques for incorporating logical
connectivity across systems. Such techniques combine ideas from the fields of database technology,
communication technology and expert systems technology.
Organizational issues cover the process of making controlled changes in complex organizational environments. The prevailing theory of inter-organizational networks explains the multiple forces that modulate
behavior of individuals, groups, and organizations. There is a need to develop focused standards to serve
as the foundations for neutral representation as well as for the development of more elaborate standards.
The problem of distributed heterogeneous inform; tion systems occurs in many disciplines ranging from
manufacturing to banking and from maintenance to logistics. A number of government organizations
including NASA, NBS, and different agencies of the Department of Defense are faced with this problem.
Within the U.S. Air Force, several efforts are directed at finding solutions in this area. In order to attain
success, it is necessary that government, industry, and academia work together to develop common
standards and to fill major voids that exist. A plan for concerted action is developed in this report.
FI~GURE
9 (b)-:
SAMPLE DOCUMENT -
CLASS 2
ATTACHMNT C
eeting (May2 4 ,1985,Toronto)
Materials for IES AdCom
Suggestion
For IE
M aterial--A
Society
Enhancement
Objective
To promote enhancement activities for the purposes of increasing IES metership and improving and strengthening IES functions, image, and effectiveness.
Overall Plan
. .1.
…aw
plan
1
i..
t1e overual
.. _
o
;__
…
….....
..
;..:
1
_.._
nnance our soc3le;y InVOIveaS
IES EMBERSHIP
addressing four areas as follovs:
8,000
By 1990
1. Method of Promotion and Scope
2. Goal
3. Enhancement Activities
4. Assessment of IES (status of IES)
Each of the above areas will be addressed
.
in the following sections.
1.Method of Promotion and Scope
The enhancement activities are aimed at invigorating
our society. These activities encompass all aspects
A!
-.
-
wnlcn are
I1
1
I-
-1…,.
L
-
-
-…
- -
,-
&E
-
-
i
- - __
lkely Co improve cne managemenc ezfciency
of the IES AdCom and they extend over a wide range.
The IES's GOAL is a membership of 8,000 by 1000.
This barometer will track
progress toward that goal.
To promote enhancement activities,
there are 3 promotional methods to be used singly
or in combination, depending on the nature of what we are doing;
(1)Top-down managerial activities from IES AdCom
(2)Technical Committee activities which extend horizontally
(3) Section/Chapter activities which have a bottom-up effect
...
§ Society activities with team work and cooperation;
FIGURB A
(c):
SAMPLE DOCUMENT -
CLASS 3
see
Fig.l-1
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. IE-31, NO. 12,MAY 1964
112
TABLE VI
MICROPROCESSOR OPERATING SYSTEM SUPPORT BNCHMARKS--STACK
EXEA!SER [81
Ta
I
2
3
I0
laO
200
Tia
pb
N d
Pu
tablu
vim-
h
(ds(c)
(e
2
I
2
Il
2715
343
13
2U 1
3440
12427
Total
(1)+(2)+(3)
4
O
44
p
TA
hWE$&
r
(ddU
=0
434
3332
4236
13
5336
7938
10320
23S9
971
1.00
Ptrfenanow nuum Ho
3
3.4
,.
TABLE VI
GENERAL CHARACTERI OF 32-Btr MICROPROC!MO
Mt ago CM
MLOW-.h
TICnoLO
I.as
1OMWO
CMOs
4.lIIS
O.OP1F
uASON
SIZ OFC
i05l 0.SIPAI
T
PICOMIT
W4ASI
C X C
o
1O00.
Qf IlOIt
nSU
4.3
219.0.0 O
MAI n
4.4
./@
WATT
A I IMS
U ACTIM
,4 TOTAL
1II r1INC
4 WATo
.rm
I
,l$ L
2.5 IAVAoIW
Po4
83
l mII
zSM
t4 ,
GIOINSf
h0 0F 0
NSITnCTO4S
moOF ACOOMs S
t. sm.r,
10.003 MII
6r
([1
II
LL
A
l"lmuA
IN
V156
u.IAI0o
16
GEIP
2
TOvUs"
2
i1
10
S
VW
vlSIIM
'Curnteuy for iteral use only.
TABLE VMI
[61, [171
EXECuTm N TM S(21,1
inMAC14
LANGUAi4
TIM( I
IWN
SIZI
S*nCN
S4EvlE
ILLISICOIO
PLZI.
ACs
4
70
C
PASCAL
tUNIX)
PBSC(VMS1
32
32
32
14
I4
14
20
220
20
9400
11 w
1 .53
C
PASCAL
PASCAL
32
14
I2
47
53
50
?4
10
960
37.100
32.47
32.320
61000 itS6MI)
PASCi
PASCHAL
5
32
3
15
Itg
244
910
9200
80*60C MWl
PASCAL
14
73
7?4
44 000
111
"s
4700
47t 00
l~.11t710
I6
Is tMM)
'32'4EL 3 II Mtl )
502346 NltM ,
02"r IC UNlt)
NP32-'
CPU,
DA
16
*
PASCAL
IS
1
PASCAL
1
11
at
PASCAL
32
Na
"A
SCA.
32
NA
NA
710
11 0
12.323
27t0
200O
91138
2213
7311
1774
t35
7
259
24
"ot
(Is MNHt
NIsI1Q3 (?7mtNl
prolowpe.
'Indicates expcrimento
"Indicates vendor-pro'ided nfortiatio.
be monitored. Consider, for example, the machine tool application shown in Fig. 1. There are several independent variables
to be controlled and monitored as follows:
a) rotation speed of parts,
b) lateral movement of parts,
c) lateral position and angle of each of the cutting tools.
To increase productivity, it
must operate concurrently;
tool interfere with another
variables can be monitored
but the overall concurrency
is essential that multiple tools
also, at no instant should any
tool. Each of the independent
by a separate microprocessor,
of tool operation will be deter-
mined by the ability to maintain noninterfering profiles. The
latter aspect is dependent on the communication bandwidth
between the computing elements. The overall performance
of such multiprocessor configurations is affected by the
number of processors, the communication mechanism among
the computing resources, the characteristics of the computational workload, and the control program. Whereas the major
constraint in single processor systems is the speed of the
processor itself, the major constraint in multiprocessor systems
is usually the speed of the interconnection mechanism used
for communicating between the computing elements.
Fig. 2 shows an example of a multiple microprocessor-
SAMPLZ DOCUIMENT - CLASS 4
GUPTA AND TOONG: MICROCOMPUTERS IN INDUSTRIAL CONTROL APPLICATIONS
113
Fi. 1. A typical machs tool pplicaoa with mudi_ too,.
LOGIC OUTPUt TO
CaT ON PANEL
TO MACHINE
Fig. 2. Computer system configuration for machine tool application.
based multi-axis machine tool control that ses a shared
common data bus. Machine tool throughput s imited not so
much by the number of microprocessors attachable to the
shared bus, but more by the utilization under load of the
shared data bus and data memory. The memory utilization
for two typical functions executed under the multi-axis
machine tool control environment is summarized in Table IX.
The total utilization figures have been broken up by principal
P'IGURS.9- ():
microprocessor tasks. In each case, the program code resident
in each microprocessor subsystem has been optimized to make
minimum use of shared memory. For nonoptimized programs
in which flags and status registers are maintained in shared
memory, frequent accesses to shared memory are essential.
This often has the undesirable result of driving up memory
and bus utilizations to 70-90 percent. At this level, queuing
contention will have a severe adverse impact upon system
SAMPLE DOCUMENT
-
CLASS
5
III
47
sample Class 4 document is shown in Figure 9 (d).
(e) Class 5:
Integrated Documents:
both text and images.
Such documents contain
A typical document of this class
contains multiple columns, with several charts or
illustrations within each column, as shown in the example in
Figure 9 (e).
The above five - tier complexity classification scheme is
utilized to evaluate a broad range of products in Section
Six.
The five sample documents shown in Figure 9
were
selected after extensive study; they are used for the
purposes of evaluating products and validating the taxonomy.
7.2
Document Ouality
Since the performance of a scanner is highly dependent
on the quality of the input documents, it became necessary
to carefully control the variations in the quality of the
documents used in the benchmark tests.
Although it is
difficult to establish rigorous measures, documents can be
broadly grouped into three classes, based on their quality:
(a) Low noise documents:
This category comprises original
typewritten and typeset documents, with normal leading and
clearly separated characters.
negligible.
Skewing is absent or
In addition, these documents have no
48
hyphenation or kerning.
(b) Medium noise documents:
This category comprises
original laser printed documents or high quality dot matrix
printed documents, as well as good photocopies of such
documents.
2%).
The contrast is good and skewing is low (under
Further, the characters do not touch each other.
There may, however, be some instances of kerning,
hyphenation, and uneven leading.
(c) High noise documents:
This category comprises second or
later generation photocopies with broken segments of text
and characters touching each other.
Usually, there is low
contrast and skewed text.
The above three - tier scale represents one measure for
defining quality.
Strictly speaking, quality is a
continuous variable with multiple dimensions (e.g., quality
of characters, quality of background, contrast ratio and
amount of skewing).
Consequently, this scale represents a
first level quantization of this variable.
7.3
Recognition Techniques
Recognition technique is a qualitative variable that
tries to capture the sophistication of the technique used in
the recognition process.
Various recognition techniques
__11___1______________________
III
49
such as matrix-matching and feature extraction offer
different capabilities for reading.
The implications of
using different recognition techniques were examined in
Section Two.
7.4
Man - Machine Interface
In order to minimize the total cost of scanning and
editing documents, one important factor to consider is the
interface with the reading machine.
A higher-speed system
that requires special skills and training of dedicated
operators may, at times, be less desirable than a lowerspeed system with a very user friendly interface.
Two of
the evaluation criteria that represent this variable are
trainability and document handling.
In addition, it is also
important to examine the interface between the reader and
other computational equipment.
The concepts outlined in subsections 7.1 and 7.2 above are
independent of the equipment utilized.
Also, they do not
require an understanding of technical terms.
Document
complexity and document quality are therefore good variables
for an evaluation exercise.
Such an evaluation is
documented in the next section.
50
8. EVALUATION OF PRODUCTS
The number of scanners available today runs into the
hundreds, and it is very difficult to generate an accurate
and exhaustive comparison; even if it could be generated, it
would soon become obsolete.
Consequently, in this paper
only a few representative products have been analyzed.
The
list is as follows:
(a) Datacopy 730
Low-end scanner
(b) Caere Corp OCReader 500 Series
Hand-held scanner
(c) Scan Optics EasyReader 1720
Low-end scanner
(d) DestScanners PCScan 1000/2000
Low-end scanner
(e) Sharp JX-450 Color Scanner
Low-end scanner
(f) Truvel Truscan TZ-3BWC
Low-end scanner
(g) Kurzweil Discover Model 30
Low-end scanner
(h) Calera CDP 9000
High-end scanner
(i)
Scan Optics ReliaReader
High-end scanner
(j)
Kurzweil 5100
High-end scanner
8.1
Results of the Evaluation
The results of
Phetests on these products are
presented in Figures 1-12.
Figure 10 depicts the relative
performance of the systems in terms of a document complexity
III
AL
0o
LL
0~~~0
UF
y~~~~~~
0
0
o0
I-
U
*
*
.2
-
I.->.
z;
*
a:
0g
z
- ~~~0
C
0
00
LU X~
CL~·C
U
t
~
0
-
0
E
u~~~~~x
~
COI
a:
- o
X
_
t
a
0
o
0.
C
V'"
1*
*t O
*
Z
<:~~~~~~
C~~
-
V~~~~~~~z~0
LLI~~~~~~~
00~
~
r
~
Co
2w
0
0
~~~~~~~~~~~~~~~~
*
* 20*
0
0c
0
a,
I
L
LUI
u >E
=0
00
z_
zE
I
zo
"e
·
,
o
-E
0
E
51
versus document quality graph.
In Figure 11, relative
performance is measured by graphing document complexity
against scanning speed.
Finally, Figure 12 maps the
performance of the systems on a column and graphics handling matrix.
A more comprehensive comparison appears in
the Appendix.
At the low end, all the scanners were able to read
Class 1 and Class 2 documents without much difficulty.
Class 3 documents were read most successfully by the Scan
Optics EasyReader and the Kurzweil Discover Model 30.
None
of the low-end scanners was very successful with Class 4 and
Class 5 documents.
All the high-end scanners were able to handle Class 1,
2 and 3 documents.
The Scan Optics ReliaReader was not as
accurate as the Calera CDP 9000 and the Kurzweil 5100 in the
case of the Class 4 document.
The Class 5 document, with no
clear separation between text and graphics, was handled most
effectively by the Calera CDP 9000 system.
In the case of
such complex documents, the process of editing and
reconstituting the original format is time consuming,
exceeding ten minutes for the sample used in the benchmark
study.
Characters that touch each other and those with broken
L
to
a
U
IZ
z
01
:
Z
O
:
_
iAS...
_CE
C
,,
0
O
6
Q
_
o
0
U
0
0
a
OCLC
0F
anow
0
'
C4
cJu
y
r
0
Q
0
a
Z
*
.~0
00
O
(3U
-
0~~~~~~~~~~~
I
LU
.
c
eO
U.
;
°
LzoUo
1
E
a
o
0
IQ
0
0
0
0
o
0
mo.
00
O-0_
.
c.OS
z x
o
!Qa
aI
:
z
a. -J
4)
I-
N
Zz
L :
I:
0
~Sd
S
0
Ul
Sd
c a
0
m
-m
U.
w
. o
o
(0
0.
-.i I-
a
=
(0'
-j
cJ
Lu
0
,od
0J
0
0
t-
A
o
a0
_
0 0 C
0
ar
>
I-
am=c
a
e
·
a
o
0
Q
a
O
c
a S aa
.
0. '
a
>0
z
ca
_
C
O
Uwl
P=
mr
0 .I= l
l- C
aC S
52
strokes were the major sources of error for all the systems
tested.
The complexity of the document being scanned
severely impacted the scanning speed and the accuracy.
The
accuracy of all the scanners was found to be highly
sensitive to the quality of the document.
Even a
typewritten document caused a significant number of errors
in cases where the quality of the documents was low.
single handwritten mark or even a speck of dirt is a
potential source of error for the reading mechanisms
employed in most systems.
A
53
9.
CONCLUSION
The image scanning and character recognition industry
is undergoing a period of rapid growth in which different
technologies and concepts are being amalgamated.
The
traditional frameworks for classifying various approaches in
this discipline have lost their relevance.
In this paper, a
new taxonomy was proposed. This taxonomy is based on the
characteristics of the input, rather than on the application
or the price of the product.
Based on the quality and
complexity of the input material, it is feasible to predict
when off-the-shelf technology will attain the maturity
threshold needed to motivate the adoption of these automated
technologies in a particular industry.
III
REFERENCES
[1] All About Optical Readers, Datapro Reports, Datapro Research
Corporation, Delran, NJ, 1989.
[21 Ullman, J. R., "Advances in Character Recognition", in
Applications of Pattern Recognition, Ed. K. S. Fu, C. R. C.
Press, Inc., Boca Raton Florida, 1982, p.2 3 0 .
(31 Market Report on Imaging, Frost & Sullivan, Inc., 1989.
(4] Shurman, J., "Reading Machines", Proceedinas of the
International Conference on Pattern Recognition, 1982, pp. 10311044.
[51 Lashas, A., et. al., "Optical Character Recognition Based:on
Analog Preprocessing and Automatic Feature Extraction", Computer
Vision. Graphics and Image Processing, Vol. 32, May 1985, pp.191207.
([6 Baird, H., Simon, K., and Pavlidas, T., "Components of an
Omnifont Reader", International Symposium on Pattern Recognition,
John Wiley & Sons, 1986, pp. 471-493.
(7] Masuda, I., et al., "Approach to a Smart Document Reader",
Conference on Computer Vision and Pattern Recognition, McGraw
Hill, 1985, pp. 550-557.
[8] Kida, H., et al., "Document Recognition Systems for Office
Automation", Proceedings of the International Symposium on
Pattern Recognition, John Wiley & Sons, 1986, pp. 343-362.
[9] Hou, H. S., Digital Document Processing, John Wiley & Sons,
1983.
[10] Suen, C. Y., Berthod, M. and Mori, S., "Automatic
Recognition of Handprinted Characters - the State of the Art",
Proceedings of the IEEE, Vol. 68, No. 4, April 1987, pp. 469-487.
[11] Harris, B.,
"Characteristics of Imaging Systems", Byte,
March 1988, pp. 76-87.
(121 Mason, J., "Error Rates of Imaging Systems", Imagina,
February 1987, pp. 42-46.
(13] Johnson, R., "What High-End Scanners Offer You", Computer
world, April 1989, pp. 17-29.
(141 Tanaka, Eiichi, Kohasigushi, Takahiro and Kunihoka,
Shiamura, "High Speed String Correction for OCR", Proceedings of
the International Conference on Pattern Recognition, John Wiley &
Sons, 1986, pp. 340-343.
[15] Wilson, P., "Error Correction in Sophisticated Scanners",
Imaging, May 1988, pp. 78-89.
(161
"Survey of Scanning Products", PCWorld, May 1989, pp. 43-49.
[17] "Networked Systems in the Automated Office", Business Week,
August 1989, pp. 45-56.
[18] Report on Scanners, International Data Corporation,
Framingham, Ma., 1989.
[19] Buffa, Michael, G., "Neural Network technology Comes to
Imaging", Advanced Imaging, November/December 1988, pp. 42-50.
[20] Kapoor, Ajit, "Electronic Imaging and Information
Processing", The Office, December 1988, p. 56.
[211 Dollard, Tony and Odorisio, Linda, "State of the Art - State
of the Industry: A Review of Commercially Available Optical Diskbased Engineering Data Systems", Proceedings of the ATI 87
4 5 6 - 4 7 8.
Conference, Monterey, California, February 1987, pp.
[22] K. S. Fu, Syntactic Pattern Recognition and Applications,
Prentice - Hall, Englewood Cliffs, NJ, 1982.
[23] Nandhakumar, N. and Aggarwal, J., "The Artificial
Intelligence Approach to Pattern recognition - A Perspective and
Overview", Pattern Recognition, Vol. 18, No. 6, 1985, pp 383-389.
[24] Rich, E., Artificial Intelligence, McGraw - Hill, New York,
1983.
III
(251 Mantas, J., "An Overview of Character Recognition
Methodologies", Pattern Recognition, Vol. 19, No. 6, 1986, pp.
122-134.
[26] Ward, Jean Renard and Blesser, Barry, "Interactive
Recognition of Handprinted Characters for Computer Input" IEEE
Computer Graphics and Applications, Vol. 23, September 1985, pp.
231-240.
[27] Berman, Ari P., "Image Processing With Embedded Expert
Systems", Advanced Imaging, No. 15, November/December 1988, pp.
51-60.
[28] Jones, Tony and Safdie, Elias, "Scanner Selection - More
Important Than You Think", Proceedings of the International
Electronic Imaging Exposition and Conference, Prentice - Hall,
1987, pp. 341-351.
(29] Brobst, Paul L., "Fax Networking", Proceedings of the
International Electronic Imaging ExPosition and Conference,
Prentice - Hall, 1987, pp. 512-522.
[301 Stern, Randall, "From Intelligent Character Recognition to
Intelligent Document Processing", Proceedings of the
International Electronic Imaging Exoosition and Conference,
Prentice - Hall, 1987, pp. 236-245.
APPBNDIZ
CHARACTERISTICS AND CAPABILITIES OF THE
PRODUCTS EVALUATED IN THE STUDY
III
O
-.
=
a4
I
l
l
I
4)
I
r4
V
Ca
4 0
Q
1.4
>4
4.4
o
00
(U
1D
.,.4
-4
a
02
U
04
4)
.0
1,I
U
a
re
a
(U)
o
0
a
z
z.40
T
1: t
l
I
I
U
SJm
.4
(0
C
c
on
C.)
0
0
0
.4
Ln
w
1 4.
U
o
e
Q4)
w
l .4
C ., -
1.4
4-4
l
c(U
4) -.F4 C
..4
m 01
o
C
m
re *
SU
4.
O
"
C
V
a
.,4
o u
O: · ..:
·
ad
0E,.
0 w
>a
I
4
· ,'
.
0
cJ
cU
o
FO
0
02
u
C)
1.4
C
.,-t
u
41)
0
0
(l
cn
0
m
4)24
O
4) O
UW
4)
.j.)4UQ
0
.p4
4)
4-)
C
-
a'
£
4.1 4.1
4.
.1
1.44)
3
W
a4
3
4)
a
CN
,-p4
1 .i(CU U
C
02
ea
Q
Q
4)
0
E-4
a
z -n n .C'
w
U
;3
z(n
0
H
C
't
=
Z
cX
=
I_
. -4
r~
0a0; W wu
L-
-J
C"
C
-
1
~
m.
_
Q
.1-s
4
U
0)
I
I
m
-,- :-":
.
u-
I
I
CU
E
C
I-
c
-
:
=
M:
=
4a4)
s4
-4
cn
M
-
0
U
"·
N t_
C ,,
.
L.e: M U
>
C, -i
.e
54
0 0
c4.) .
O
E
u0
W
0
Q) C
N
,t
-
)
.-
A 14 . (U
M
a_13 Q1W
Q a
Q4 > )
E-44
C 0
u
4)
02
02
I .,
-,1
X
_
>4
E-4
QJ
E
0) C
0
z
a4
3
o
a
w
4)
U
o
r
cI_
z
04
LP £ O
Ow
Z u
.0
a,.4)
3
U
rn
41)
(U
cn
.
.4fl UN
4-4
O
O
(1,)
,
ul
134
·(U
O
3
a
U
a
4
l
0
0
0
;3
I
.,4
0 4J 0
0] ,
X
w
3
4 (U
O0
4J24
In
- -
0
wt
0
C
02
4
04
1
02
.94
O
P
02
4)
0
arlu) .-
C
m0
N
24
4)
4.)
1.4
1.4
u
o
a
>1
m
C
9
,0
.. i
M
C W
c
02
4)
31
c:
C .-s 4)
U -4
3
z
4:
- --
-,
(U
.4 .,4 ,' 14
43
0
a4
02
02
.0
E4
-
.1
U)
U
m
U
.
w
41
I
4
W cU
I .1
I
E
43J
04
I-s
W
'-4
C4 M
-4C
0
z
O '
1
4:
H
~z
w
mC.
)
u
0 0
14
oU
Ca
w
'-4
E.4
4 )
.
ea
o
>4
-4)
0 3m
O
O
(U
N
0
O
O
-
3
0
z
CU
02
az
I
0
Z
W
$UCC
0
0
Q
2; V
u 4)
W4
4)
3
a
U
o
j k
0
j
C
X
(U
(U
0
4 Us
O
0
X
r
V
4
ol
E-4
0
O
0 m
Q
S -~e
$
~...
O
U
I
Y
U
>4
n
4
4;.
'
0
t
a
4
x
Q
4
E-4
e
1
44 OX1
0
Ln
-
.)
Q4
0
4.,
(
V
U C0
U;
41
44
,.
C(1
*I: 4
o
O
O
04
O
4-)
0
z
Q
J
x
Q.
t
x
C
41
W
I OL
v1
P4
E-I
4
U)
W
c0I,n
-
"
0
II
O
:32
I(U
,.~-4
aj4
u r
r,
m
'
uV.I-co
31
4
I
0
00
'-4
L4
41
j
o
W
*
u
cS
; -H
' S4
3.- U
oc
CD
c
M
o
aQ
Q
a
40
.-
O
v
u
C
"
si
m
a
(
oo
U)
w
1%
CZ
on
o
0
0
m
0II
N
.u. Q c4) Q0
>O
o
.,
C
L
Z
O4]UL3a
U OQ
O
U
44
41
O
U
c
*"
3
0X
e,
X
·Q1
4
~'--0U4C(U v
_4
V
C
QO4
O
o
. *V
E- :
0
U;
>i4 O
o
4
fi
0 ra
(U r- O
0
Q4
0X
-41-4
u4
.)xn4f
co
24
X
41
:
051
. ... X
(.
cQ.
Z4
I4
U
u~
U
11
m
m
0
4
CO
U
S..
E-4
,c
w
a
u
4
0:
4-
u
4
U
C
UI
U
*H0 z:
U
4
X
·-
o
c
~
(0
(
2
UO
X0
v
iE-
m0
(U|
[
>I U
a
X
b ______
r0
mo
a
Xt
5
_
<*
"4
Q-
Q1
,
U
U
Ia 0
O
Z
X
u4
E
>4
I 3c
0O
6
xU tD
tie
H
:
4
3.
c3
:-
0
-
O
x
U*
(
o
,\
m
I
I
I
z
.j
4
'Q..-
N
!1 3
il *
ii n
'
5
n~~0
H
:
W
1
i
-
C
U0
X
1
3 a
O
Q0
La
4
4-
.
4S
W
00;
Wi
41
3
>
i
mrl
)
I
c
3
3
~Ji;|XXEv
Qf
~4
1 3
i U
0
i >
3
U0 4 1
i
i
W
|
V COc |u~ =
3
U
I;
-4
U
X
3
0a
OCi|
I
I
Q
N
0
(
.
I
111
I
l
I
-
--l
w
I
-W-
OCO
43
3
O
s
C
0
I -,H
CO
$4
r. z
$4
w
·- U
4.) 0
4)
O
0
PC
(lo
4)
O C
4-4
4
r.l
0 .-4
0
=.4 43) 43
0
la
c
w
-4
-H
43
CZ
0
cn
C
CN
x 2C-r
0
s 4 OQs
0 0
000
I
Ec43 Q40 $4
0
4.
43
$4
4
E C E P
a
0
$4 -H
04)
..)
*,i ua
m
E
I
-H
O 4
O Q
003
4:04
)C
EOU.
431..
4CO
e1
204
04
N
i
·-
CA
04
o
0
W
4.1 -H
i
.,
0
O
~
.
·- H
C
w
O
C
0
O
9-H
04
0w
u
CO
C
a 0
O
w
W3d
3
a
0
cn
H
ill
4 C
O
31
N
0--a
E-4
H
4
4
zci
U
04
44
CO;
E-4
43
C
C
rU
M
H
C-
4
W
4
.
C
MI C
.,-.I
Q
L4
O4 f
cn
(v
3043
44
r-1
43
$41
C U)
O
CO
u
.F4
,-4
3:
444
m
u
0
uO
0
0
O
0
>4-)
cq
-cH
(u >LI3
0
z
.$4 .q
O >4
04 4.
$4
o
iH
C
J,4
Q
00
-
VO
O
Ez
U
H
I
0-
l
I
_ 0
--0
0
M,
C
0
'C
X
, .C:
EC4
~Cz Oc
j
C O
4(O
OE20M
O
0C
C) M
H -H
OE4;(U
U
.,t
-
O~..
(l
1-H
H 4
>1
-H
4=)
.-H
.-4
04 3
0cu
4
V
(
-4O
C-;
Q
4 4s
4-i
-,.
N .H1
I
1
(U
I
04-40
W4
H
Z(U
.)
H
Sm
o
O
43
.0
C
0
O O
0z
L4 44
C
>4
a4 ~3
-H
- 4CO
U=
0O
u.
H
o C 44
3
o
O I
43
'UO
a0$4
to
l-
L0 L4
>4
m
4.
4
$4
C3 *,4
.r4 u
o43
4-
O C
o
0
0 a4
u .-H
M Co
$4
.
(U
0 .,
0
CO
43
04
-H
EO .
0
0
I
(4
(O
$4
4i
$4
4.)
0 $4
0
U
o-Ho
.,-
0rq
I
CO U w
4.
E-r
03
z
4
OC
u
CO
,)
CO
44
o
o
o
H
C-)
0 4-4
O C
l
2
0
04
w
En
04 0
-H
C
r
O
$4
O -H
It
Q) $.
: 3
'040
4H
MI
4
-
- H
4-n
*,
'U
40
-H O
0U a
-a
-H4
C
-
O
cM
n43
0. 43
. 43
4-4
O m I
Q400
Z b
l
$4
0
I MO
$4
3
3 14
N 4
-P4 Q(
0
Z43$4
E;4
o
43 04
I
0
4-i
a3
4
l
' C 0
a C'
Z
w
I
0
.,.-H
0
0
U
Q
.
O
O
$4
(1)
I4H C
.,D
-H
>1
-H
H-
V LJ
u
0
m w
-4-)
m ea W
44
I
0
IC
II
(n 0
__
I
E-4
4
>
0O
4
M 44
3m
O Cci
o
eU
i
43
mv cC
NQ
|
ra
I-,t
Q
0
-
0
<
U
re
cr
-li 4
(t
$4 0
ss
a)
O
0
O
m
lu
-
0
u~ o-
,i
QC-
o
..H
(U
$4
-
.
-
0 a
4
'UQ
k
a.4
O
V
i
14
02
4)
I
-
Q
CZ
0- a44.
~4)
ON
0
03
o
,,-
02
*4
4401
HE-4
0O
0
oO
0
a; a
L)4
I
CO0
4441
O O
C4
I41 u
4P
1 0
4
> 04
E-~4)
¢J
0
4£
0
1I.4
S4
m
4i
4
I- e
0
14
H
0
·n
EE
C
4.
03
z
4-4
3 UIO
X
0
CO u
" U c
H
0
·.
0
4) 02 44
0
o0
E-40d
c i
'-4
IO C
.I
E-4
U
a
0
¢4
3
U
O
0
0
Z
u
4)
uz
C
m f
(0
4n
1
03n03
1.41.
'.04)
Oa
4.a
0
I4)
O
0m 4 si
L 14
c )U4;
cm
N
04
E.
ro-4
.4
04
r-o
*-4
C3
V
cu
l lI
0
N
HU
Cu
4 I
la
4-
J)4
1.
Cd
Ca
H
H
E.
L
4
E-4
a _
E4
'U
9:
C,
u
V
:
4c
I
a
l
m e
Iu
.r;(
Ir
n0
m <
4)
.04.1
05-1.44)
s
c
cNIC)
C. 4.1 X
i
_
w
c)I
ui
.Q1
2
1I
I U0 O1V
4
0a
94
!-
-4
C
=
2:
u
-W
4.1
4)
I
-
4I
C: _
3CO
-i,,
8~2
-3j M
m
0l
L
<
IOS
;4-2.0 n
4:L'-O
.
HH
04
I
4. ,<
Zn
4)
O
I3
U
H 0
3)
a)
a0
Eu .,.3
O =
-
''
i
1i
" W
1Z" C4
"
¥CI]
> =
>
w
;1
'r
zI.,4.1a1-5
'
m O
=
D E-4:
"
I.
0
4
. O
Z
-
.a
C
) 0
3
rd<
z
C IT
_
I~v
=4.1
a) W
4U
)
>
C)
*0
4.1
H
I
_
X,
041
Mr
I. a0
'1-2
(U 0
!;
a7
EH
n O
)
4- .0
t
I -a
3-
u
,J
0
=
Ei
~~Ot
3
C
<
=
w
i¢
I =
L)
(
m
.
L
a t
0(U C
102
C
0 a
1e m 4
III
I
$4~~~~~~
04 **
.-
-¢
U
e 41
^ 411
Q *H
ur
a
.o 0O w 0
Cde zra~~~~u
;r4 a
a
a
pt
A:
O
CA
a
r
iJ aa
.H a
a
a
c
O
m a a a o
O)
OI4Cc
U Q,'rL
c,
zr C
s a
C4
PC
. a
.-- ·^
r- . ra
.0 0.
MU
10
a V
O
a
O
~J·~~.'a
0
3> a Q
; CTe Ca
S.
C0 4i
0 M
"
,.
·
0
a
03
I
L
I _
1 44
C.
a re
rj Pue e.,Oo
31
edO0
uC3
a V
rar
UW
E-4
4
a .H
U
si 4· -J
ffi
o
C tv
DA
ocn
0
1E-4
HI
°
O
a
e re
)., U
0
S
c
0
Il
M
Z
c
Q) a
0o
k
v
v Q
.
4
aa
·*H
o
-W
u
H
r4
· 4~
a
a
X
O.
E
av c
*H o~
F
.
a
r·
k
W
ol
8
Yr
,
rn
3
wc Cn
0
oQ
_~
wo
C
a3
o
H
·c,.C cnI
.H
Q)
- 024~~~~~~~~~~~~~~~~~~~~~,
W-
o
Pcn
~
P1
C
cJ
·I-
*4
HI
go
4
PE4
z
ra
U
u
oou
Q
ru
U
su
I
|
;
Q
or C.
m~~L
x
^4
c
(1i
L
en
a
En
uQ
0O
a
X
fi
H1
E-
O
CT
a)
X
O:
Q3
or
a
Z3
V
Zr
0<
z
C
O~~~~~~
c,
Ed
34: H
:
> C;
Z
E·
~~~~~~~wS:;
a
OmO~
0
n- 32
f
C;
£ crc >4
(
.rl
ao
u .H
-
C
a
n
·4 M a
~~~~~~~~~
·
x o
3- ·c
W 4
-
v'
SE
E
I
u
QaZ
C
zO
V
WWLa
W1
> X1
a4 a,
m
Mw
m
E
Mc
4 -J a
_,
c,
4£i
LI U
O Ez c)
1 X
:
E
a
UX
M
E-4
<: w
s
_a%
Ua
· ti
Q) a a
P
(O
c
a
--H 1~3
r-4
X
X;c x
C
4
ro
a
_
X~~~~S
E-4
w
e
eO
c
0
a
z
4
(s1
o
(1
mm
O
:>4
o
m 37
w
C
O >
O
v>
c
0
44
Q
-4
-
III
-
.
3
' -0'a)
u
·P4
a
I
(4
P-4 n
(U)
-.2
4
02
(U
0
O
(U
"-4
a
m
O C'
0 44
9-co
IV
CV.
'V'Vj
'V
4o
o
'Vi 14 C
(
O
02
m
m
0
V
o
o
> 3
'u o4 "4
'O'
C, 0 4
4_ 'V
02
-(U
-4 (C
0
'V
II
-
0
W
E2
04
0
cn
-
m
4
Ecl
: -4
-i
4)
(U
u)
-4
a
O
ha
-4X 0M
.,W .
sw
4:
4
o
F
0
O
Z
41
m' '
S
m
-C
at
.O
tv
0
w
.V
U
Im
C
0
I
0Z
-4
.i
C
4.,
w
el
02
o
0
N
L4C
U
IV
uu
946
U)
V-.
IV
M
U
:0
a
Zn
'V
4r
--4
la
04
L4
o
0
I
,-4
r-qmr
u-
"C
IC
0t
'V
.I" 4
>4
IJ
'V
C
_r
IC0
i5(U
C
91
04
A
02
'V
I-4.X
4
ru
ra(U
4j4
-11
14
3
. -4 .9-s
'V
14
C
-4
(U
'V
-4
.,Q
2
'V
a,
co
,,,,,
U
wl
E-4
u
u
C
O
:r
_
1: Q
o~~~~~~~~C
r
o~~~~~~~~~~~~~r
+
II*4q
0
0
u
ul4
I
0 ,
la.~
-4
IVO
ro~
la0
m
.,.
U'V
90
X
PC)
cn
2
V
c0
0'
(1
(U
a
w
02
,t
9
0
(1U
m
u
E
ru
5.;
'z
0
o
02
w
C
C
4.
2
.-4
v
IU
C)
w
.4
w
z4
U
C
.r4
1
z
w
u
CZ)
0
I.
II
-
oc
E-4
I-s:
".4
0
.I4
4 0
Z
-
-
V'
1,-4
ol
Im
.9.4
o
m
-4
4
0 ad
m 4.
I
W
4
.C
,4
N
4
(U-I
1-4
3 b
N a
.^
I
Qj
a
'r
$4
N
w 4C
om
0
C
a
L
9-0
m
C
I
1
3 r4
02-".4 m
m4
(a a4
i
L4
m
-
4-)V
tis .-4
m
I
.0
-
.
Io
O
w
4-
va,4
I .-4
2
L4
0
W
Z
s
(
C)
I
!
u
I_
n
z
44
0
.z iC~~~~~~~~~
'
4. 4
,
I
aA
CO
o
14
0
Z
0,c.
,
< C
=
0
u
a
MOZC.) r
_ '
Cm
'n
mN
14 o)
( a 4-4
N
(
·ta
PILI~~
';t
Im
IU
I,,~
Z >.,.4
0
0
U
>C
I u
a)(
4
S< V
I
0
IM
II a
il
I
,,,
ft
V
U0
5-
' PL a , V
-V;,
i
.4
Q.
I_
w
=
z
N
>iz
O1 VO
M
M -
g
!.
O0
-
M3
0
Iz
Z
4
--
III
I
.I
.
c
u
0
.r4
40
m
1.4
CL)
4)
I 0
4.4
at
0
w
*1- 1I
:10
4.)
X
0
0
0
C
q
-4.
0
m
(a
a 3
O
C 0
_4) CP
p
4)x
3.
~4z
(U
4.
0
z
Q MI
-(U
MI 4-4
E
(U
W
Cm
3
z
Q
4)
4-4)
,
ma
D
4)
IQ
(Un
-o A
mo
4.o
41)
4
02
40
co
>4
-
W
1-4
E-4
2
c
.14
S:
0
E4
H
H
c
U
m
U,
-4
W
Pc
W
CD
a
0
cn
H
E
4
H
A
w
X
0,
uU
H
In
0
o
3:4
0
4-)
aU
I
0
024.3)
4O
0
43
z
1:
CU
-
-
-
-
Po
4)
.4
:94
34
0
4-W
a4
a
ec
.r4
(U
.,
4J2
a(U
3
.
lz~
a
a'
.0
:4i
4-)
(U
.--
4)
>4
ow
l
f
41)
N
C.)
0
C0
0;;
4)
UZ
E-4
W
Enr
0
0
la
O
rl
0
X*
E-4
z
-4
ru
02
0
.94
U,
V)
41)
O
0
0
D,
S
4c
.94
V
E-4
VE
X
w
U
o\o
(3O
;
:
1.4
H1-0
a
CU
O
Ln
0(U
CD
1
z
U-,
0
ad
M
3-4
L4
4.)
4)
>4
ro
a
:
Z
5:
-
i!
0
C)
~~~~I
V
4
O
W
'4
v11
.14
0
>4
W-
l
4U
P
!
-
1
~
,
>
.4
O
·H
4 _
'¢
_
-)
<
-
iI
'
O
o
r·
'r
3
(d
5
.
;.<
a
->
r=
H
O
O
O
3- 4_4
:
Et3
C
.~
O
W-
a
Z
O
hr
__
E4
z
4.3
u
at
z
C)
_
-
3
O
v]
.)
H t¢
E-4 H
c- E-4
3 "
> CZ
~
mn
4)
0
>
2
C
Z
:CW
=
m
~!
·c,^
4-:
I
i
a3
z
40
.3
E-4
J-----V
O
:4
X
02
4.3
u5:
2
0c
0
0
aLc0
I tv
U,
04
C)
(U
0
4.10
U
0E
-(
X
O
m
O0
0
s
o
UO
_ a
( C
4 V
V Um
C
a
a
I.A a
L4
0
rQ: Z X
H
H
cn am -W
>4 2 t
= :Z C
0: =:
H
)
14
C
0
I4
(V
a OuS
a4
4-)
x
1.
O
IIN
!
:
_ ,
_
H
_j
1
.
4)
4)
_
:> )0
O
'04 oQ)
>1
. .'
-
a
0
X
II(
4r0
o
im
3
N>4
Cm3 11
N.40.)
.>O
Z0
Q
m
O0
>1
3
4
U:1.~4.)1 P
S
;
1
.4
(0'4
0 0-.
J
¢)
Q4
a
,a
4C
't ' 4
0
*'
C
I 0
(U
0
l a
I
:
.1
4,
, 4
1
U
0
0.
Z
'
.J
0
:::.i0C
(
4-
U
U
4
il
t
.
4
4 0a
w0
.
4:
az
cJ
E4
Cl
0
U)
uz
U
0
Im
YC
a0
;
04
tn
I-4
H
E
:3
O '
I
'-
I
C)
01 ,'0aC a
4-4aU
C
30e4
04
a0 P
, ..
0C
(U
4) 0
O
l4.C
04
0 4
E-4
z
(U
o
N
(U' 00
O ~
,
0
.C
)
c
4:
U
rd
z4Ed
U
0
___
Cl)
U:
U ,
'-4
O
o
r
I
>
E-4
I
: ·
I
oc_Z'
Z
4Q)
H
IQi
La
U
02IU
E
.
9
44
IX~~r
cn
(I
4,U
'V
4>x~
,
x
S
DI
0)I
_4
w0
w
;
E
0n
a) J
:
Z
1.4
Q
o -4
0
z
C
4.
%
-
a
.,,
4CO
t
(U
0o
0.
I 02
U X
e
4:
U
: n: 1 3 V
I .,
=
E4
E
U
__
I
Z
H
(
Q
I(
Nn
_
c
' 0;
O
C
-V 4
UCS4)(
I
4
U
H
OA
VE-44
X£
)
O
* .4O
u4
C
(
.'
*
4-'
O
U
-o
3
-'
-4
1. 4
UZ
3
U
III
I.
Ir~
·o
~
~
I
0
,...~e
c:
.^ I
O Q0
· CO
O
.
.4 >
H
Q,~~*'> U UV
3
O
C
V
M,-I
0
(U
.
'
r
C
Q-
L
0
H
I
02
affid
u4-£
cH
m o
H
m <
0E P002
;
r4
L0
m o o
m
o
.
C
0,
(fi
ol
V
W
.~
o
c
'w
o L
QQ0
,J..0
0
44
QV (
4J
m
C
x/ a
4
·
(
.
O
14
0-4Q &0(V
4) m
i
= O | OQJd
NO
4)
Le
C
CO
0
w
O 0 L.
.i
I. 0,..,
I 41
· rll~~~
'~I
V -i'
3
Qw
o
O C W
r
a0
o
3
X s
k._c
w
E4
w
w
0
C4
U,
W
4
H
H
w
E,,
H
V4
I-..
w
Ic
*
q
a
0
W
0
I -
0
(n
U
0
04
04
o
U
O
04
uZ
0
E-
:
t..~
a) a
00..
~c~
4C
W
4l
-4.(U
~.J
CQUO
m 0S2i
H
m
0
(U
DU
'-4H
4-)
U -)
o
(
I*4-
,-H
0
0
_OXO
I
. (Uo
0V
O
,
0C
.
- °
c £
I M r.O
1
.
I
0
4 ) ar·"
.V
e0
0
Q 0,-0
a)
0
4-
. O,
L
1
0Q
a,
0 ~QQ
.
-
.
H0 .
.S
0 UV
-X
4e
l
<*
4
0
.,_.O
(
0
m
O
4,0 0
0.. C
0V
w;
C4
I-t
I
O
4
E-4
O
I
aU
ct
zU
u
Uw
04
o
E-4
V
u
II 0 o
(U 3
,
H
Z
rtj
0 .4-)I Z
C)0
O0
: aeo
cn4V
V
ae
0 4 - 02
Po
O
4.
m
c~( w
CQ
.Ha)
C:
U
£
O
I
u
(
;cr
O
E-
l
V n
uz
4VC
n
I
a .
u: O m
I1
I_
_
III4
E
Z<
>
H
Eq
_
-
Zn-04
Z
I
,
I
,
0-
H .4-JS4
tD i
uz 0 O
4i
C
4i
h
N
O
0
0>4 -44
o
0>oo
la
u
OL
, ,
0
MIu2N
C
v
0
0
0
V
O
O
C
O
ul
wZ.CO
0sW 4ia
'0
O
W
3
C
V MI =
E
Z
3
0O
O 4
a0
O
-'C
Ul
O 4
Q
1_
a _ s
_
3
n0
C
:D
C 4> uz
EV
W >
'M
WI m
V
; *r
k-4
o
_4
_
T
, o
· , II
ao
.
oil
C:
·
r=~~~~~~~~
;.,
N a
En
MOI
V
Z
wmO, i,
4
4-
4)
~
a
4w
4
o
r0
3
0
PC
a
rn
U] r
a
a
tvH
X
C I 4
t_
a v a C a,
*H C
IC '
V
U
Vo
% Vz 4-)
. 0O
O
=4i
C
O
1 O II a
~~~~4~(
o
v
V
.
> e
0
3
L4 -W
4
04 >
C:
c:
Q$
a=
raCJ
~
1 ,.
43
4c
a.
4
0
.rla ~
.,.. ~
.,4
.
C
.,,
mu
-*4 f-
0o
Oa
OP e
C
c
Y vl 3
Z
E-4
)3
. ·1~
· ~~~~~~~~~~~L
,4
m; CO
X
0
we
0 c EQ
o0
a,
C
o
O)
134
w
=lcn
V)
o
b
a ¢o
C t_
Q
a
Io 0
O
"O
u
X~~~~~~~~~~~~~
4w
riE-
Me
cOc
E
ul
v34
UqX
cn
Ed
ue
w
E
,
. >
Qq
ad 4)
L4 :4 r
0 .,<
LI i
O) 4i
a
u
J
O
1
4·-r
3V
Nf
cn
C
i
a
a
Q
--
c
C
z
0
0
sw
(1
.dn
i
~
~~o
41 L4 1
:.
oO
. 1·
Z
.
V
. r4
mU
>
C
J
L4
f X
m Li
V
0
*
I
3
VUEf0
F O
o=
.~Cn
c 4)
a
X I _-
O
H
£ O
:z
0 3-'
:
0
Q0
>
,
Z
u SC
O
r:a)c
o
3
.
O fi
4O v-
6 U
O f
m
U
>U
(l
4
3
_r az
rl
*H
·-
W
:
C
a
I
3
fi ·^
_)
3
¢@
4C)
.CI a
z
I4
t
0
3
Q
uL
X
3
I
U
V
©
v
Q O
a
V
-P4
a
Q
a a
4>;~~a
L)I
C
>
3 4
v
C
3
-
.z
O _
Q
b
(Y
4;
~ 4.a
4
W
w
a
~
C
>4
-
u
c)
0--·
0>
Q
a
4
C=L
c lo
.
e~~~C
V
iC- Qu C to
a0
v ca
-
I
W 04
E
a
C.
uO
S
E-4 4
4
>
W
t4
) A
;I
Cd
O
>
04
0 E-4
M
aJ
E-1
IV
z
:J
Zx
:
4
C
S
s0
l
3
n
0
a
L4
4-)
o
>
o
U
z :4 (d
III
0
c4
N
O
UO
E-0
·m
I
Cc
m
E-
Vd
1-4
54
-4:
Z
U
0
I
84
0
m
la I
>
ov a I
V
08
O ·· I0,
HI V
e
,1
Um
co
z
z
02
· .
I
Q
0 I
:)
o
.
s
. 4)
0
, 0
Ch~
t:
E
c v
2n.,~
0
0V
0
n
0
Q2O
I
0
00
.
0
O
0
I
4J
0
.4)
O
.,4
0
0
E
C
00
.,,
z4)
0
u00
Q
mO
0
s- Q
m
3
41
u
1
0
W
54
Co
H
5.4
O
wd
rnH
-4:
U
E-4
cO
Co
Ed4
:::;
~o
X
u
cn
01
on 4)
m
I
I
u
u
: 'I
i m
C
4)·.
' 4
(.~ M
3
ct
0
I
a
U -
I
Zo
o
m
m
U
It
o
I
ou
I
,
0
O:
c0
,
x
x4i
c-
,
>1
"a
e
.,q
^
a)e
re
Caoa
uo, -
a
cl
ox
I
a
'4:
-
o
01
-i 0m
.l
44
4-j
0
c
uz
C
(U
O
4
E-4
k
O
4)
5.4
Co
0
H
u
z2
4
04
E4
54
U
C
cn
E-4
Co
l<
I w
cn-
Oc
CI
4-
4..T
4
0 1"
U
a
440
-
s0- 4)
4.0 -Q
01
u
>
O
zuz
0
u
I
m
:zw
Cf)
O
I
-vC e-
30
|1 C
o
4
o
c;
Er
*-
0
P
wE
zr
oO
0 3; c · 0
U
0N
4)
4-
u
I
I
u
accJ
ii
C
_I
I
u
z
z
:;
Uz
0 ;=
U
I
(S C
(o
1 4. 4
m-"
4) _a
. I . *a
, 3t
14
~Ii 4
c,
0 ep
cn
02
v.
u o
m 43
n
E.-1
H
Ac
Ei
I
n I
4>
0
_-t
4J^
>
'ra
a)
a>
C
3 C
I
m
Im
C
al
H
0
n
C121.4sie
E4
f3h
1-4
_
C
>
Z
U4
4
z
1
PC
nC
U)
E-
co
n
-4
u
OI
U ocnI
tCPo4O Crr
a
l
_
C4
(a
4
a
C
Ilr
·a
#¢
U
tO
U
Z
0
~D
0
a
I-I
04
n
r:
I
U
14
Z
a
I
0
E-4
nd
A
0
C.)
'
a
4 4.,
.
>
>
>
r
.
'
0
4,
e
CUt
a
40
4:
4
4i.
C41
E(-
>)
.
0
S
0
0''
0
4.)
X
02
di
tU
ra
0
cn
H
En
c)
4
us
u0
0
OCO
4-)
O
4
4)
14
H
H
C
)
C)
4.)
c
1.
4
4C.)
I=
4
C.)
uHr
14
U)
Z
*a
(n
0
a
C 4w
Q
O
.J
W
=
O
C
410
in
M >
4
m
z
0
H 44
E4 H 44
;
"-_
*-4
.4 3
H
h
3
[3
3:
a
.4 X u
w
O
a
0
a:
0
0
"
4-
*
o3
o
_oO
o
C13
a
)
mi 4
U
E-4r
0.)45
OU
E4
4-
4ZZ
s O
Z
<
E
=
Q4)
14
Po
H'
C
uu u
H
(
m
4CUC
Q1
a4
a
Q-
a
a
.4L
h
Z
-44
>i
3>4
O * 4 U4-W4
40
0
O
O
4-
-4
O
*
Om
.4C
> a
wU
C
>
Uu
4)
E
02
-4
P1
4)
>4
0 O .
am4
)
I V
i
tV.
4)
CL
14
H
C
4
0
ad :n1
u .4
0 a
a
III
klc4l
'1
°
C
m
3=
I,~
cX
I
x
ee^^4
a
0
V
i
w
I _~
U
ca P
O
a
._
21
oc
g
14
U
cx
t
N
E
Vv
av
I .,
I
VJ
e
n
0
P1
m
I
·
fa4 ~
.,-+ "1n^Ca
I
I
a
I
H
m
o
si
oOa
c
O
O
3
-o
a
us
C
0 4. ;
Pa
O
~
r·
c,
C
o
04
c4
o
·
3
Tn
COmo
u
0moa
. , C
Xa
e
c,
all z
vI
C .~
a
.,-
m
a
r
OC
,
m o
so O
'r
CD
C
a
WX s
0
s
e
C
W
Z
is
H
Ed
~
2:
0s:
.~
3 i
W
4= .
.
X -W ) 4
z
w
En
w
m,
,.=
.,. z314
4r4 ·n n3
-I
4)
-r-4
--4
-- 4
a)
04
I4
ra
O
0at,
vc
To
0
w
a
O
_1 . E 4
a
~*<
V --q C
a
E-4 CZ
04
z
w
4
u
Cd
a
V]
c)
U
To
W
a
ii
a
E
W
C
OI
X
I
a
:
cn
U
I XX
14 5 44
E;
r
I
:zZ
45s
v
I0 m
t/co
H
a)4
(U
I9
-
-
3
C
4
u
4
41
In
c
Q
w
H
a
I
)
I
I
0
O
Q
aC
o
0
n
Q
s4
Pc
vt
a
4t
3
H
at
U
v
o
c,
o
4-)
o
V]
H
>
z
|
°
H
;
c4
a
.,
Z
1
.4
Eq
r
<: P;
"
w u
n
34
O
S
£
X
9
5
4i
JW
O
o
o
0Orc
4
m
aS
~~~~
~~~4-
.i
'
a
L3: Pt
3 a4L
4i la
$4
3
Z
ft
ED
3-
E4
0
3
0
O
,
Ef
3
U
a) uS
X
CN
rn
zI|L
~~~
} .~
3 cP
Pi Pi
s4 i
O
ae
(
::
c
Q
-
~~~~~L4Q4-
a a
a(a c,
ql
4 4
M
71
0O
O 0(V
O
44
9
Sk M
o
e3 U
;1 P
a
£ Q
4
1 r
a
I
XI
Y
Q
POq
V
C O2
m
3 0
UC
4>
-
4 4
O
>
r
w
V3
ce
·*
cr I a: I
I:
EV
OW W
0
;
c
w3
=
D H
01
rr
at ra
&
4
--4
w-
4·cm aM
a Q> U
0
ms (U w 4
W4
IV
Oaa
W 0o a)
X
0
4-)
zU;; £
m o
·4
C
11
s=
to
4J
4-)
c,
W
x M -4 0)
x e *H
Q
3
1-4
(1
m
U
S:
E;X
CD
<5
a
O
L4 C
P1 P1 a4) 4i
Q0
m
E-4 -')
O
k- 0
ew
F
tV >
Ora
o
4
O
C
-4
·rr-4
la
a
r4
U
a
c
a
m 41 u
,
0Q H'
a
w 4o
e-Ha
O
.-
0
p,f
Q
P1
ff
I
~
c
H 4) 4Li
U
a
~
'X
m-H ;
ae
Q)
i
O
W $s 4
4
I Im
U
1
~
I
gX
a
Wa
w
| m 4-4
I O
4uz (UA
04.4-
m
3
CT
a*H
-4 Pr
UZ
I
Ev
M
O
W C
I
v)
~o ~
u
cn'r*H
^£
0
-
U ( A>
I
-4
I
-U 40 -'.3 raQ
a
o
I
etao C sS4
a
a
u
U
3,
>
4)
I -4
>c
H
HH
E-4
C)
E~
0
X *9 c
3 *·
m
i.00
(*.E
4;
Q0
O
C
4 43Q `rn
mm
w C
e o
I
:z: tn
in
E~
.
CO 4:O iQ a -4i
H
:
W
Z 1
cw
,,
a CS:rs
X I
4 I
H
Pd
0
E('Oe
Ia
<*
I5
2
a:
4
I3X
q
"01 C
>,
Mt
u
.I
"0
r0
b
E
C
_4
H
io
~
4)
0
tU:
-
*
0X
H
4
-4
0
O
Q
C
.)
C to
X
_
<a
_E
0
0
V
C
a0
02o
>4e t
LI
<QO2
4
V
a
::I2
4 I 0
3
0
w
4-
-i
- .,
V
X4.)4
0
C
r
>'
>1
4
Ix
>
0 O
> ') 0Pi
C
.ad
4
c,-, 0
s
N
C
-J4
PIEt
.
_
,)
2
C
VO
"
t
C1
0
0
0
w
-
C
rj4)
04
4
4J
02
E-,
z
4)
5.
4
rC)
0
a
0
C)
O
P1
-t
4
tuN
C 0
02
-Eu
&4 (aJ
e
C
n
-
C
'U
I
i
I
0
0- P
,'
4)
0
W
C
v0
1
*"m
.i
a
I IH
',~
-I.
0
r0
Eu
e
a
I
C
cn
a
O
eo·o
C)
H
L
.U
'
02~
0
o
0
- c
X
r-
00
c4
C U
' W
0
0
_
o~ o_
______
0:
E-4
z
Ca
U
E-,
rz
Z
0
.3
4
a
.
tCT
i-
4.1
Eq4
Cu
E
E- -4
II
O
X
z
X
:T
a
O
_
_c.e-
C.
)3
02
I3
'4
UC:
St
_,00
.
:.-
>
C
E4
I
U
V
4
a.
tz
I£u
|-4 X o
>
U
4
H
0 -W
EH
02f
Z
0
LI
02*"
N
u
(-
w
>
C
>
_
4
! aM0
OL
W)4
1
I 0O
£I
*- 3
E
to
Ot
4
4>
-_
V
O M --
;-c
.e ..-i
OM
0 -
0
C)
44
C
1H
L4
4j-
V
.
4 .1-4
1
I
V
La
.4
4-1
:.1 C
,'
O
2 CL
W0
0
04
_
V
0
*
0
I
0
O
t
44
o
x
O
I,
"
u 4
0
_
c.
4)
_
I
N
.
a
.
C
£
C
40
4
>4
"4X
co0
.,
a
I
'.4)
O
4
0
E
*
,
40
_
Z
4
UC)
·r_
u3
O
I
-
_.1
(a
4
t0
0
I
.
*
a-
V
0
0
z
Cu
C Q)
V.
O
_4
_
II'
I)x
e
:
Q
1i-
m
I
U-
.
H
0
U C*r~r
3
a
'1
Io
_
0
00
O5
.,.I
u0
0
4)
( . 44J
~)(U
e
0
4-
a
:
_·
4) er
*i
)O
am~(
0
C
O
V
LL
e
EI
2.l.1
,,
o
(o
a
a
H
'4
a
v
E
zu
in
O
-0
.)
Q.
a2
0
)
:4:
a
H
HO
I
'60
Co:°
ao
I · *w-4C
aw
I
9
0
(
| z1
*,£
I
X
O
0
)4.)
ti
0
(aO
0
w) c,
0
4Q
c(
e 00
0 o( >p
C)3
Q
04
£;
sw m )ou
0a
;r
rr
c
2
0c
£
o
u
C
0
O
O
e>
Ps
O
Z
O
0U
1:402
4
(U
I .U
a
#4:
0
0
Z
x
0
Co
E-4
z
w
V)
H
I
c
C
I
C
c
C(
*
O u
(
m
o W
£
J 4.4)
m
4
C0C
VI
02(Um 4
cO2
I4 zZ0
v mY
o
CO
En
3 z
0
v
a
-.
(UO
ci
o
Z3
4L-
H
4J
4
i
4 C
U
C
-
4:
4:
C)
ZiI
a
'-,4
E-4
Z
E H
¢ H
(
I
O=
J
U
0
-4
-
C
SZ4
W
C
J
-
>
z
E-4
0
E U4
v
I
2
0
#
C.)
0
1___
C
O
4.4
4)
__
-
-
III
III
-
ZA
3
4
'44
E
0
w4
4m
4-
.,4
~4-
0
- k
0 01
4)
,4 4
k~
C
-04
vs
cn
E
I
w
e
U
cr4)
Oi
02 m
U
U
41
S
5.4
02
m
a
0
4IE 3 On
O
P,
Cu
'02
4)0
I
II
I
I
4)4.4
Cc,
;
N
4
C
Cu
4)
4) e
4.
'-40
a .
L4
02
C N
a k aCu C1
mC
£4
_- V Q O c
-.i
'"4
C nP ,·
4.2 Cu02
0
Z
02
4)
0
4.)
0mV
L4 4
9
O
CS1
0
CA 0
N 44
,
4.)
.
r
02
4i
C
C. N
=
'4
m
> 014
s ve 0 sm
ad
N N 04
I
4)
O
O)0
P
O
m
4) .)
O
3
3
0'
,-
s.4
4)
14
4
0
0
4
S14
'01Cu
x4a 04
4
4)
4, '..
I,
4)
"4
4.,-
4) .-4
C
4i
a4.200
4)4
o
4)
d
'"4C
cu
:
C1.4
C Q1n 4) C
00
5w
0 e
0
Ill
4)
m
.0 14
PC
VI
tj
II
4
e
1402
I
Cu
I4U
M
14
Q
'
_
'I-N Ct
14
0
E-4
>
|
02 C
03
C
'4
l
'"4
ad
W
0
U
Q
M
02
E1-4
0o
u
.I
1..
0.,4
4J
0O
Z414
Q :> h
4)4)
2
0
00
0%
0
-
4
aO
L4
m
u
l
rn
o
Ct
4)
w
0
4w
Cu
(O(
0
E-4
C
4)0
( Cu
'4
W
al
O
0u
>1
ua
0
Cu
".4
14)
14
0
14
4)
N
0
CP
"4
--
Jll
Cu
41
o
I
'"4
E4
O
o
oo
4.)
0
cn
H
E4
U
u
'"4
C 3
0
0
4)
O
C
)
4
u
.4
0
u
'4
ci
.r4
z
4.)
'"4
C14
14 4.
0
3
4-I
41)
0 0
=
To CD
~4
O4
w
4 4)
.)
To~~~~
Q4 c)14
W
H
E-4
u
:0
so
.,
4)
4.2
4-.
4.)
Iu
4.
02
w
4)
14
0
4.
C
0
z
U,
0
0
0
z
z
z
0
C
0o
0
3
0o
CD 0
0W
i
'4
_
3
Q)
,It
Z
CD0n
O0
1E-3
:D
04
4
I>
i"
z
I J0
0)
I
ou
u:
C)
oC
0
C
0
0
*H-E
C
10
o 0<
cwS
14
>
U 40
v=
ln
· .
-4
_
Q
X,,
4
oi I
e
0)m
m--
N a
>
03
_JC
.-
et
O
0
z
0
C 0
0
4
Z
4-
u
=
V C
=
Le 0
30
02
N
l
04
I
E
U
C
.f-4
E-
02
c
o
4)
Ct 0
4X ,
·- 4 .a
.,4
a
=
a
0
C4 >
4)E-4
14
0
o 0
C.)
O-
M
z
O
4V
A: V 4
.,
a
III
I
l
l
l
C
E,
1-4
L4
0
c
14
O
C4-e
m
I II
.
C
OI
I
4
O-
0w
z
C.)
4
44) OC1.4
)
0
C=
0
.i
"-
o
4
Cm
C
0 44
:C
4
4) 4 4 u )
XL
H H Q
V reSw
E C
U0
0t-La
r
U
1:
4)
co
in
01
)
0
U,
1.
(U
0 O C
C
0
C) ::. O
O
O
Z
o
E-4
I
(
02
>4
;~~~cau4
-
o
0
4
0
03
4.)
I
ad
-
--
:<:
C;
02
C,
C
O
,14
C02
O >
v
H
Ez
.z
c,
.H
O
C
C.)
0
0
43c
0 a04
14
1.4
C"'!
Xl
0
C.
4)
u
.,4
I4)
Cn
.4
4.)
0
cv
5:
-
1e
4J
E-4
2
QU
E-4
c
" C
E-4 'O
C
-4 aOM4)
-H
02
4.)
4)
0
Q)
rC
ai
:3
4.
I
V
ci
c
0
('1
.,
0
CA
rr
4)
>4
u
U
*
0
4)
C4W ) L
'-4
2
Is IC
.*H
coX
4)
Q,
02
-
40
(c
CO
4
-
On
C
U.)
0z
04
I
014
rOr
0
o
a:>r
4)
14
O
4
0
co
4.)
S
C
Q Q
u L
O 4
4. ,
: .
x
4)
4)
Z
.1-4 Q
4-) 44
I
O
u
CI
4.)
X
,4
4.)
1.4
-W
E-4
E-4
C.)
C
-4
CD
C2
4
E-
t_
1.4
O
tZ
-
u
C
m
a.4
l"
ra
0
- -
,
4)
qi
-
J J
a.
4.11.4
4 hi
0
0n
u
1..
0
4.)
z
>4
03
m
H
=
H
W4
u
C
I;
>1
,
V3
2~~~~n
J21.
-
· cl
-
~~~-
---
U. 4
Ec:;
u
0u
I
I IO
a
I .
..
-a
QCf
S< M]
u
ul)
4.
In
4;
co
a4
I =
E-,
i04
0
O.4
I )n
l
14
C
I
>nZ 1
4u
'-4
Z
O -H
: 0
0 04
(a
O Pi
L4
LQ
0
Z C -C
14
w:
>40
0 e.- O
E
a
a)
v
u
(3) C4
O
0
m
C
4
z >
m-44
(I
-.4 *l"
03
.JZ
= >.
N
.,I
-1
a
I 0
I
4
004
IM
Xe
X:
4
-
-
I
M 4
l
U
0
-4 ,
4i ac
e.
.,
I
0
(d O C
4)
4)
I
-4
~;
u
*4O0
3 _
4
.,.
.0
n
cCm
>
1.4
i,
OC
4)
I
U 400
40
.9-4U
0
,-4
49r)'
0 -C
"o o
M-
t
I
4J 0
4
to 04 0
iC
oa
>
04
_4
-
_
X
>-
o
.r.)
la
o
to
I
.4
.4.
ea
0
z
z
I
-
-4
w
2
0
1-4
o
., .
C4
4)
C.)
0
H;
4)
w
--
l-
-
0
O
C
.,4
.9-
E4
14
E
-0-
Q) C)4 _
.,
.4
C
3d
0
0
0
0
0%
U)
E4
a$
Ca
a
u
:U]
00
0
H
W
C4
H
14
ra
ra
lz
C.)
Pe
m
0
4j.) 4)
0 4 544 W4
d4.i i
M
.4
e
0
4)
4)
0
0
0
0 al
0 C'4
o
o1 4.)
C4
4)
00
M
)
4 0- .0
3
4i C
."I .-
0
04
>4 0
%0
0
l
w
Ez
W
'4
C)
pi
Or
C C
,4
w
I=
0
4.
r4 O
u0O
0 0
C C
O
I
|
4
U)
H
%4
0
4.4
C
4.) 4i
1 m
ra
(L
>
'r4)02>4
-I
4) L
o 2
.4
I4 4
C
-
x
9:
14
C.)
H
9-4
cn
E-4
4:
C
4x
u0
C
C) U ,-
-
IIII-
a
-I
+J4.4La
o =
4)
U
.,94
4.
Ifl
4)
0
.94
m
'4
m
m 02
W
-4)4
m
>
_
3
4)
0.
4.
z
4
41)
I
3
E4.
L4
an
)
=(e 02
0.4n
2 042
>1
>4
'IO
E-4
M
4..4Q
O
0
-
4 0
N -4
·4 4.
9
U4
O
-j
.
I4
OE.4
i;1.
1
I
I -W
4i .-4
C:
O
~U
-----
---·------------------
4)
0O
.4
v
,4
=
2
C
O
1.-4
,
>4
w
I
w
C
>1
. 4 4.) .4 $4 "
(J i)
4
UO O
0
tOZ
V
in
lu
4rLd X
I
C
0
.94
-4i
1.-4
O
.4
>
QU
_4
-4
C
I
4E
0"0
w Ci0
Q
.,4
U
I
4
CI
e
0V
H
o0
4.0
0
"44&
2
0
-4
.)
54
C)
.9I
.-
H
X .
4X
Q0 Q
024
4 4)
C)
2
.4.)
0o
-4
w
w*
0 4.
4)
.9-4
--·---------------
C
II
C4.)
,.,-i02X
4
O,M
.9-4
114
I
.9-4
1..
Z0.-
C.
u T
.-.
I2u
---
C
L4
P-
co
4
4)
.-4
eO
-
0
C.)
O
-
4) -4 Il
4.
0
14
u
C4
4
4-4 -
)
*,r
C.
E4
4H
C
. L. 4) -
mI-
cI
a
>
;
-
4.)
4
4vi
e
0
4.
0
4.
I
0
o
-4 C
0
#"4
Vj -
o0
o
.94
3
m 4)
w
co
0
m
0
4) 4)0c
01t
4) ..
4
.,4
4·rl
C
(O4.)
I
H
0
a
C
.4
Q
4)
.J 0
0 0)x
2 I 4 .
S4
4)
1.
4.)
4)
I
4) C
U
m
0i
I
.,-4
4.
H
E-4
II'
-
-
0
a) >,4
S
*
al
0
'0
.4
r'0
4.
E
1..
4)
C
V
(U
Hl
a
H I a
.,q ie
C);
U]-H
g
nlQ
r
0
H
0O 0
0414
(1
1: 0
c
0O
4)
0
I (
0
E
Zd
H
0
0
o
0
C
4)
14
H(U
021
0
1-"
0:I 0
OCOt)
'00
C co
a
4)
4.)
(
u4)
(U
H Oa
C)
0
m
0
Nr
mV
MG
C)
.
H
0
4
s
V _
aH
.4
.
2
CP
I
ea
ea
m (U
.4,
4
Q Q
ra a a
3o
(U,
o
o
O)0
)
-.. D
O
04e
C t
P4)
1.4
m
Q
W
4. *-(
'0
- 4)
4)
4)
C4
o
0
(o4)
C
4)4.)
0
44
4. 1
0
m(U
0
,.4)
m, Q
4.) .p-4 0 0
.9-4
..
00
o
02e 0
0 -0
tv
440
(U
0
=
0
0
o.,-3
4)
I
r.Q
>1
(U
C C
Q) (1
r.
4.
O*H
(Ua
.
j 0-
l
0(UF
41 _,
0
3
04
C.)
04'0
4
0
0
U01.4 I
04)
(a
i
4.-
04
C)
2
.,-I
w
0C) 4.J
u
a
l
U (
U,
l
I
0
I 0
W
wo
1.
0o*..4
o
la
4)
a0(U
3 C
M M
H
02
-
0 e
4)
UZ
E4
4)
-4
'U
4)
0
E4
co
4. )
(1)
la
S4 U
I
*^
Ca
Q
Q)
4.)
4
< UMwq
4
0O
4-4
.- 4 .
.-4
04
43) oQ 0
1 C
0
X ( U0 0Q)
W 0 4-) 4.44.)
0m
(-, .l-Cml . C
IC
.
m
v
04
C)
a
E0
n
ix
0
u
:
E
En
02
00
C.)
0
O
X
30
4)
0.4
.,9-i
0
30
V
0O
.
4-J
4)
04)
4)
04
la
0aQ 0
U
0· 4H4) U0 0.'
0=
41)
4.)
t
4 ))
o-H 4(
u u
o
0
0
4.)
w
0
0n
04
..
0
k9co
m M
Uo
z
t
W
0
-
H
4
4
a%
1.4
Le
.6,Xn
wO
U
Z
a
z
o
4Q)
L
4)
H
E-4C
<
E4
H
di4i
la
0
4)
O
z.4
C '
4C
Q CN
AJ-
C
c2
0- 3U
>
LnI
3
C4
U,
4 A
I
-W
Ca
Q-Q
O
U2
(U
0)
m
M m-4) 1
o
. V 000 3o2 .) -H
S
O~G~.~
.e
~ (U4W ~r
4
4-4
9-4
4) (
Q
£=
Z;
OC)
H
H
Q)
14
Ul2
0m
'1
0 4
-H
04)
0
0
;
r 4)
0
0
0
3
H
04()
X
c
a
3
3
C
0C0
00Q
a4 M
I- 0
04
4i
O~3
= - l
0
. 23(U
Q-
wEn
rr;C
0
C)l -
C%m
4
0.
a
4-
S
I
a
I
95
Le
s
XX
O
I
a
J no
II
H
anx
rd
Z
a,
i-4
D,zco (a
E
u
0
n
La
aQ
X@
4) &
e
s;-X ."
o
X
V
IV
SrlW
c w
o
m
o3
a
,-O
0
a
o
I
Di
w;
O
0
C
Z 4i.
3
C~ -4
.rfI
I
It
a
0
Q
4J -O
z
4-..
>>~ .
0
.
i
Q4
I
1
S V
u
Io
Es4
V
I
I
A;
o
o
(n
C.)
I
I
C
rl
.
*H
....
I
,.
_0-f
M, 04
XXUO
I
O,
Vr4
*,
v
)
>: _
X
X
I_* Q
_
e
~~~~~~
I"
~I
O
.
-3
O
.H
;
a
a
u
(
q
.:4
X
o:
L
3
-q
L(
vu
c
C 1
L rJ c
C
,
tv3
QX
tX
oe
e
C
V
.
m
4
a
0- Z
0
3
0 a4
0 V; cX
C
O
r4(
m C
I
;
o
Z
"
3
a
M
a
4~Z?
4
X
n
4~~~~U
ss
v
C
0
Q;P
i
X;
X 1
O
Z
O
4
4
i ;
~1
DI=~iZ
:
r
uz o 1
>
-W
a
OJ C
s,
i
X
L,
___
4-
*4
u
V
£4
;
Q
-
=
0
ok~~~~~~~(1).0
W4
- _-4 _- X
Z
$C W
E;
Q1
a)
*$^
ct
I
4
a
er
n£ o
z
·c
E
--4
>Q
L
Z_;/
a
0 a 0,
Q W
X
E
I
w4
C)
4j
Q
(1
;4C
Le
*n,
I: s:·~
. :
X
a
a
m
>
,
:>
V
i
E-e
.
.
a
3o o
o si
4m
>i
eD
.
O
ra
a
I *
0
44 W,,,
,
a
X
I1 4;~- re · O; a) · C1
rl {3C
.^ Q~(1= 3
k I .<,
Z
E-4 (z
4 4
aa ft a
I
I
O: :
Q
X 4i
I
a
M
*
a a O *Hr·
a o: sr
H
(J
in
·
a
d ·*-4)O I
X-4Z
0
>1
44
&
Q
,
.,
O
r
~"
C 0
0
a
P
e
C :4
O
1Q
Cm
W
E-4
u
,--
3 C
.
/,,4
Wd
1
4
Xi
*H)
3
C
,,-- O
i
:
.--
.
/
C 0
· ,I
0Ow
Q
:: *'
c --
1
0
G
.
·
ss~ a
a
O.
o3 IO
M,-~,-~.
>
4" 4J
0.>::: .W
C to
a r4
c
.0
O
o0
4i
_~
I)
V:
·
.-4
pqC W<3
.~
I
o
o
Z:
C
a _P · r .~,
:3,
:
Q ad
m
I
V
I
I2_C a
I ,-0
un
z
S 440J
.1
0 4n
wt 4
o
X
-
,
.J
e 0.
.1
J
O
O
C~
to 4,)
O
U
P;
I=:
4
az
I
I
'd
4-J
0 F.
~1
OI
Dm
w
j
* 0
U r·
:z:
>e
EP v
$e
re
W ED
4 7(
4i
aa
;X
i
_
Q 3
oI
Ed
n Pr
W
C4
o0
·c
,
a n
o
I~~~~~~~~~~~~~~~~`
c; 4
,
Q a>4
0so4 Q
c I-~
o,
a
I
~
i
m
Ei
pq
(1
cJ
~
O Q
-
Q
toX
Or