Form Image Compression by
Template Extraction and Matching
Jianguo Wang and Hong Yan
School of Electrical and Information Engineering
University of Sydney, NSW 2006, Australia
phone: +61 2 9351 5338
fax: +61 2 9351 4824
e-mail: jwang@ee.usyd.edu.au
Abstract
This paper presents a generic method for compressing
multi-copy form documents using template extraction and
matching (TEM) strategies to reduce the component-level
redundancy in form document images. This method is
consistent with the following principal steps. A template,
ideally the same as a blank form, is extracted from a
number of filled-in form images using statistical
superpose and prototype matching strategies. The
properties of form documents are investigated and
strategies for template extraction are developed. Next the
filled-in data is extracted by matching the template to
each filled-in form. Several possible situations are
included at this stage to deal with different cases in
practical applications. The template and the extracted
filled-in data of each form are coded into a lossless
compression format. In the final step, a generic rule is
developed to reconstruct the compressed form documents.
The compression rate of the proposed form compression
method is mainly dependent on the relative size of the
filled-in data compared to the size of the template, which
is generally small for form images so that a high
compression rate can be achieved. By avoiding document
segmentation, the proposed method is effective for all
types of components, including form frames, pictures and
text. Experiment results on different types of forms that
show the performance of this compression method is
excellent.
Key words: form image; template matching; image
compression; redundancy analysis.
1. Introduction
Document image compression is becoming more
important due to the growth in document scanning and
transmission. Technological advances in processing,
storage and visualization have made it possible to
maintain a large number of form documents as digital
images and make them accessible over networks. In order
to do this effectively, an efficient compression scheme is
essential for both storage and transmission. Document
images are mostly pseudo-binary and rich in textual
content. They are represented adequately by binary
images produced by scanning. Forms are documents
typically used to collect or distribute data, in which a
large part (the blank form) is the same. A large number of
forms are processed by many business and government
organizations.
Previous researches [1,2,3] provide useful hint for form
image compression. Some standards have already been
released for binary image compression and facsimile
transmission. The first comprehensive standard was
released as the CCITT Group 3 transmission standard,
followed by the CCITT Group 4 [5]. Both schemes use a
run-length-based approach with an extension to dual scanline coding to exploit the coherence between successive
scan lines. The Joint Binary Image Group (JBIG) standard
is a recent international ISO standard in which context
modelling forms the basis of coding. The compression
rates of all these methods are heavily dependent on the
complexity of images, and a higher compression rate can
be achieved by avoiding the repeated storing of the same
or closely similar components in all the images. In order
to develop a scheme that is practical and feasible for
multi-copy form documents compression, some
sophisticated techniques for template extraction,
component matching and image coding are needed. The
proposed template extraction and matching (TEM)
method is developed for this purpose.
As a type of document, forms typically consist of two
parts. One is called a template, the common part in most
images, including preprinted components such as form
frames, characters, symbols and pictures. Another part is
the filled-in data that is different in each form. The
proposed TEM form compression method contains three
major steps, template extraction, compression and
decompression. If a blank form has not been provided, the
first step is template extraction, which is determining a set
of components as prototypes to extract a template from
1
Compressing
Restoring
Compressed
images
Multiimage
Comparing
Template
location
Yes
Similar ?
Template
extraction
No
Yes
Filled-in pattern
extraction
Try again ?
No
Restoring
compressed
images
Display
and/or
saving
Compressing
and saving
Finish
Finish
Figure 1
Flow chart of the TEM form compression scheme
several filled-in form images. It includes image deskewing and location, distortion adjusting, template
extraction and refining. This step is not needed if a blank
form is provided as a template. The second step is
compression, in which form images are preprocessed and
the filled-in data extracted by matching them with the
template. Varying situations have to be included for the
practical application of this method, as described in
Section 3. The template and the extracted filled-in data
from the form images are compressed in one file with a
lossless compression format. In the decompression step,
each form is reconstructed using the template and the
extracted image of each form. Figure 1 shows the flow
chart for this scheme.
Unlike many lossy compression techniques using
resolution reduction or texture preserving methods that
might render a document image unreadable, the proposed
method identifies components which appear repeatedly
and represents similar components with a prototype. The
TEM method exploits the component-level redundancy
found in multi-copy form documents and reaches a high
compression rate while keeping the original resolution
and readability. Furthermore, the quality of images is
improved by the use of a statistical algorithm to reduce
noise. A high compression rate can be achieved with this
method by employing some effective strategies to cope
with practical problems such as distortion, noise, flaws
and modification. At the same time, the accuracy of the
decompressed document image is very important for
visualization. It is necessary for a compression scheme to
preserve the shape of all the components in a document so
that they are correct and recognizable after retrieval. The
rules for template extraction and matching are designed
rigorously enough to minimize the possibility of
mismatching.
The details of the algorithms used in template extraction
are described in Section 2. Section 3 introduces the
proposed compression scheme by matching prototypes in
the template with the corresponding components in each
form image. Section 4 describes the algorithms for
decompression and the experiment results are presented in
Section 5. Section 6 provides a detailed discussion of the
approach and the conclusions.
2. Template Extraction
The first step in this compression method is to extract a
template that contains the common components in filledin form images. A blank form can be used directly as a
template if it is of good quality, but in most case it is not
available. The template has to be extracted from filled-in
forms. An effective template extraction method by
2
comparing several filled-in form images is critical for this
compression scheme, considering all the possible
situations in practical applications. A series of processes
is needed to fulfil this task. After skew and distortion
adjusting, several form images are overlapped to get a
gray scale image, which indicates the statistical possibility
of each pixel to be black. A template is extracted from the
gray scale image and then is refined.
2.1. Image De-skewing and Location
The skew of scanned form images needs to be corrected
before template extraction can be carried out. The
accuracy of skew angle detection is critical for getting the
best overlap results of images. Some early approaches
dealt with this problem. Y. Hang [4] developed a method
for correcting skew of text lines in scanned document
images using interline cross-correlation. In some cases,
the skew angle can be determined from text margins [6] if
they exist in the scanned image. The Hough transform can
also be used for skew detection [7]. Another commonly
used method for skew detection employs the projection
profile to compute the skew angle [8]. Each of them,
however, has some limitations and cannot to be used
directly in this compression scheme. An accurate skew
detection algorithm is required which is effective for all
types of form images.
projection profiles of the de-skewed form image. Form
images are overlapped by moving them so that the
differences between the profiles of each image are
minimum.
2.2. Distortion adjusting
The form images of same blank forms usually have a litter
distortion caused by printing, handling or scanning. This
seriously effects the efficiency of the results of template
extraction. The purpose of distortion adjusting is to
overlap all the similar components well enough in the
whole image area before template extraction. By
analyzing a large number of filled-in form images it is
found that distortion is accumulative in both vertical and
horizontal directions. The following distortion-adjusting
algorithm is effective to deal with this problem.
After analyzing and comparing the above algorithms, a
new de-skewing scheme is developed using an improved
recursive projection profile method [9]. For documents
whose text lines span horizontally, the horizontal
projection profile will have peaks with widths equal to the
character height. The projection profile is computed at a
number of selected angles to detect the skew angle. For
each angle, a measure of the total variation in the bin
heights along the profile is made. Maximum variation
corresponds to the best alignment with text lines and
horizontal form frames, and from this the skew angle can
be measured.
Form images are usually scanned at a fixed resolution
with a small amount of skew, less than 5. A set of angles
with a fixed difference is initially selected according to
the possible maximum skew angle in applications. A
document image is divided into equal width vertical
strips. Horizontal projection profiles are calculated for
some of the vertical strips, and skew angle is estimated by
finding maximum total variation in the bin heights along
the profiles. Then a set of angle with smaller difference
than the one in the previous step is chosen around the
skew angle estimated in the previous step. The number of
vertical strips is reduced as the difference becomes
smaller and finally one strip of the width of the image is
left. This cycle is repeated several times until the
difference is just one pixel. This algorithm can achieve an
accurate result, 1 pixel in image width, and is robust to
noise and variation of form styles. The location of each
form image is measured with the horizontal and vertical
Figure 2 Vertical constant white lines produced in
printing or scanning in images
The first one of a set of form images used for template
extraction is defined as a pre-template. All other form
images are adjusted by it. An adjusted form image is
divided into several horizontal strips. Each strip is moved
vertically to get maximum overlap of corresponding
components in the pre-template and the adjusted image.
The strips are then divided into blocks to adjust horizontal
distortion using the same method. A rule is established for
the move to avoid the disconnection of strips and blocks
induced by the adjusting. The strip in which the
maximum horizontal projection profile is located is
3
defined as a base strip. All the other strips are moved
vertically relative to it. It is similar to horizontal
adjusting. This method is effective in most cases, but it is
not efficient in the images in which a vertical constant
white line exists caused by printing or scanning, as shown
in Figure 2. As the results of this method need to be
improved further, a component based adjusting algorithm
is combined in the following template extraction step.
2.3. Template Extraction
The relation between the corresponding components in
the two pre-templates is
and
Tl=Th+D
(1)
where Th and Tl is the set of black pixels of a component
in the pre-template with higher and lower threshold
respectively. D is the difference between Th and Tl. A
sub-set of D is defined as Dw:
for each pixel p  Dw, p  D & Np  Th
A template is extracted after classifying all components in
the pre-template and erasing those not classified as
prototypes. This template needs to be refined to reduce
noise and imperfections caused by the erasing operation,
and to smooth components contour. Some of the
prototypes need to be double-checked.
2.4. Template Refining
A set of adjusted binary form images is overlapped to
generate a greyscale image, in which the density of a
pixel is determined by the times of black pixels
overlapped. Filled-in data in an image have less chance
than the components belonging to the blank form to
overlap with components in other images, as do noises. A
couple of pre-templates can be obtained by choosing
different thresholds to binarize the grayscale image. The
pre-template with a proper threshold is similar to the
blank form but has some components created by the
filled-in data. An algorithm is developed to detect and
erase these components by comparing the difference of
corresponding components in these pre-templates. The
position of components is located by performing a
connected component analysis on a pre-template with a
lower threshold, which means a larger area is included in
the rectangle of a component. Before extracting the
template, each component in each image is adjusted again
by comparing to the corresponding components in first
form image.
Th Tl
components in their rectangle area, the pixels belonging
to the other components are preserved.
(2)
Np indicates the set of pixels of a pixel p and its four
direct neighbors.
The rules for classifying components as either prototypes
or filled-in data are developed by analyzing Dw and Tl in
different cases. A component belonging to a blank from
has small difference between different pre-templates than
those belonging to the filled-in data, and therefore it is not
difficult to classify them. The components that have much
diverseness from different pre-templates are classified as
filled-in data and erased from the pre-template, while the
others are classified belonging to blank forms.
Components that belong to blank forms but are different
because of their connection with the filled-in data are also
erased because they can not perform as a prototype in the
template. For those components that have pixels of other
It is necessary to make sure that every preserved
component in the template is the prototype that belongs to
the blank form. A double check of some prototypes in the
template is carried out by comparing them with the
corresponding components in the original form images. If
one prototype is obviously different to the corresponding
component in a certain number of original images, it is
excluded from the template. As a result of statistical
treatment in the overlapping, the extracted template
contains much less noises and imperfections than the
original images. The noise and imperfections not only
effect readability of document images, but also reduce the
compression rate in this system. Component contours are
smoothed to improve the effect of filled-in data extraction
and images compression. Noise in the template is filtered
using a connected component algorithm to avoid erasing
dotted lines or patterns.
The final template preserves all the prototypes that closely
resemble the components in most original filled-in
images. It presents the common components to achieve a
higher reduction for component-level redundancy. Figure
3 (a) and (b) are an example of an original filled-in form
image and an extracted template. It is noted that the noise
and flaws in the original image do not appear in the
template due to the statistical treatment used in the
process.
3. Compression
The basic innovation of the compression scheme is to
substitute components in filled-in forms that are the same
or similar to the corresponding prototypes in the template
with the prototypes to reduce component-level
redundancy. The proposed compression scheme consists
of three steps: input image preprocessing, filled-in data
extraction and compressing the data stream of template
and extracted data. One of the major tasks in the
compression section is to develop efficient rules for
detecting the similarity between prototypes in the
template and the corresponding components in an input
image so as to make the correct substitution. Filled-in
components outside prototype’s rectangle area are
preserved without any change. Filled-in components that
connect with preprinted components or are (partly)
4
included in prototypes’ rectangle area are processed
according to different situations.
There are three typical situations that occur in all practical
applications. In most cases, the components of a blank
form in each input image are very similar to the
corresponding prototypes in the template and can be
substituted. This is the basic the principle of the proposed
compression scheme. Generally, if a component in the
corresponding prototype’s rectangle area in the input
image is obviously different to the prototype, the
component is preserved as filled-in data. In some cases,
the same component, such as a digit or a bar code of
forms, exists in many of the input images used for
template extraction but it does not exist in some other
images. Occasionally there are a few components that
exist in the template as prototypes but are omitted in an
input images. This occurs when an input image has a flaw
or is modified. A special mark is needed in the extracted
filled–in data images to indicate this case in order to
guarantee the decompressed image almost the same as the
original one.
The algorithm for classifying components is based on
pattern matching and substitution. If a candidate pattern is
a good match to a corresponding prototype in template, it
is classified as a preprinted pattern; otherwise it is
considered as a filled-in pattern. This method works with
a variety of component sizes and is general enough to be
used with any specialized matching function.
3.1. Images Preprocessing
The preprocessing of input images is executed before
comparing them with the template for filled-in data
extraction. The purpose of the preprocessing is to overlap
an input image to the template as well as possible. Input
images are de-skewed and located in the same position as
in the template, and their distortion is adjusted according
to the template. This process is similar to the processing
described in Section 2.1 and 2.2 except that the template
instead of the first input image is used as the standard for
the processing. A detection program can be introduced at
this stage to check if an input image has the same blank
form as the template and to identify it as such if
necessary.
3.2. Filled-in Data Extraction
Filled-in data extraction is executed by comparing each
prototype in the template with the corresponding
component in an input image. The matching function used
here is based on the weighted XOR [10] and the
compression-based pattern matching [11] algorithm,
called the weighted pixel matching rules. All the
prototypes are grouped into two classes and treated with
different algorithms. Simple Connected Components
(SCC) are components in which no other prototype
appears in their rectangles, while Complex Connected
Components (CCC) are those that other prototypes appear
in their rectangles. Form frames and large components are
usually complex connected components.
Unlike the relation which exists between the pretemplates used in the template extraction in Section 2.3
with equation (1), the set of pixels of a component in a
template generally does not contain the corresponding
components in the input image. The relation between
them can be presented as;
R=TI
(3)
where R is the set of pixels that are the exclusive OR of
the set of black pixels of a component in a template (T)
and the set of black pixels of the corresponding
component in an image (I).
The similarity of a component in a input image with the
corresponding prototype in the template can be expressed
as
S=1-Nd/Nt for SCC or
S=1-Nd/Np for CCC
(4)
where Nd is the number of pixels that are different
between the template and a input image in the rectangle of
a prototype for SCC and in the set of pixels of a prototype
for CCC. Nt is the number of pixels in the rectangle area
for SCC and Np is the number of pixels in the set of the
prototype for CCC.
A prototype in the template is exactly the same as a
component in an input image when R=1 and they are the
reverse image when S=0. By adjusting the threshold of S,
the matching between prototypes in the template and
corresponding components in an input image can be
controlled during the comparison. In the proposed
method, weighted pixel matching rules are developed for
both CCC and SCC.
The matching process consists of several steps. Firstly,
types of prototypes and their rectangle areas are decided
upon using a connected component algorithm. Then
patterns in rectangle areas of prototypes are compared
with corresponding patterns in an input image. Adjusting
for location is performed to optimize overlapping of the
components in this stage. Weighted pixel matching rules
are developed to deal with different situations. As the
pixels in R that are the neighbor of the pixels in T are
insignificant to indicate the deference, they are excluded
from the subset Rw used for comparison. Sp indicates the
set of pixels of a pixel p and its four direct neighbors.
Then Rw is the set of pixel p that
p  R, and p  T & Sp I or p  I & Sp T
(5)
The weight of each pixel in the subset Rw is then
calculated as
Ws = (Ns+1)*(Ns+1)
(6)
5
where Ns is the number of neighbor pixels that belong to
subset Rw. The weight of pixels in the subset Rw is
summed up as a parameter to determine the similarity
between T and I. In order to get more accurate
classification, some other parameters are also included,
such as the dimension of a component’s rectangle area,
the rate of blank pixels in a component, etc.
For SCC, the comparison is executed in the rectangle area
of each prototype. If the similarity between a component
in the input image and corresponding prototype in the
template is over a threshold, the component of the input
image is erased and the prototype in the template will
substitute it during the decompression. Otherwise the
component is preserved. If there is a prototype in the
template but the corresponding area in the input image is
blank, the prototype is copied to the input image to
indicate this situation.
The matching rules and erasing area for CCC are different
to that for SCC. Only the pixels within set T are
compared. The erasing area for CCC is the set T instead
of the whole rectangle area of a prototype.
Correspondingly, the rules for the decompression for
CCC are also different from the one for SCC.
As the purpose of filled-in data extraction in this
compression scheme is to reduce the component-level
redundancy, the integrity of extracted filled-in data is not
guaranteed. This problem occurs when filled-in patterns
touch or cross form frames or preprinted patterns. The
decompressed image with the proposed method, however,
has no flaw caused by this process. Because all the
comparisons and substitutions are performed in the
rectangle of prototypes in the template, all the filled-in
data out of them are kept unchanged. Broken components
of filled-in data caused by the field overlap between
preprinted and filled-in domain can be mended [12] if
filled-in character recognition is performed.
3.3. Compressing the Streams
Templates and extracted filled-in images are sort into a
data stream with the templates being the first. To
compress N form images with the same blank form, N+1
images are generated in the compressed data stream, one
template plus N extracted images. This data stream is then
compressed with a lossless compression method, such as
JBIG or CCITT Group 4. Its compression rate is sensitive
to the complexity of the encoded image. As the image of
the extracted filled-in data is much less complex than the
original input image, the image data is further compressed
by this scheme. TIFF image format supports both multiimage and CCITT Group 4, so it is selected as the format
of the compressed file.
For N image files that have M images in each file, the
compressed stream in a TIFF file has M template and
M*N extracted images. If the templates are presented
with Tm, (m=1, 2,..M) and the extracted image with Cnm,
n =1, 2,..N, the stream of the compressed images is an
array as T1, T2, …TM, C11, C12,..C1M,…CNM. A special tag
is used to indicate how many templates in the compressed
TIFF file. The name of each original image file is also
saved in corresponding tags in the file. The compressed
stream of images can also be viewed with ordinary graph
tools.
4. Decompression
The purpose of decompression is to restore the
compressed data into new images that are the same
(lossless) or similar (lossy) to the original images.
Templates in the compressed files are firstly processed to
locate each prototype by connected component analysis.
Then each form image is reconstructed using a modest
reconstruction function that compares the template and
the corresponding extracted filled-in data image. As skew,
location and distortion of each image have already been
adjusted in the compressing section; every image with
extracted filled-in data is compared with the template
directly.
There are three possible situations arising from the
comparison. If there no any black pixel in the rectangle
area of the extracted image for SCC, the component in the
original image is the same or similar to the prototype and
is erased during the filled-in data extraction. In this case,
the corresponding prototype is copied to the
corresponding position to substitute the original
component. The second situation is if the component in
the extracted image is different from the relative
prototype in the template, the component in the extracted
image is kept unchanged and no substitution occurs. The
third situation, if a component in the extracted image is
exactly the same as the corresponding prototype, it
indicates that no any component exists in the original
image. So a component is removed when it is exactly the
same as the corresponding prototype. The third situation
increases the size of the compressed image very slightly
because this kind of situation rarely occurs.
The matching rules of decompression for CCC and SCC
are also different. A comparison is carried out in the
rectangle area of each prototype for SCC. The copying or
removing operation is also performed in the rectangle area
of each prototype. For CCC, however, the comparison is
not performed in the whole area of its rectangle, but
instead, just in the pixel set of the prototype. The
processes for the three situations mentioned above are
also different from SCC. The copying or removing
operation for CCC just performed in the area of the
prototype. Pixels in the rectangle area but not in the area
of the prototype remains unchanged.
After decompression, images of extracted data are
reconstructed to represent original images. As many
images are compressed in one file, it is time consuming to
decompress all of them. So several choices are provided
6
in decompression. Only a template is displayed if the aim
of opening a compressed file is to have a view of its
content. Otherwise selected numbers of images in the
compressed stream are reconstructed. Figure 3 gives an
example of the compression approach. Figure 3 (a), (b),
(c) and (d) shows respectively an original form image, the
template, the reconstructed image and the filled-in data
extracted from (a).
(a)
(b)
(c)
(d)
Figure 3 An example of the compression approach. (a) an original form image; (b) the template
extracted from a set of filled-in forms; (c) the reconstructed image and (d) the filled-in data extracted
from (a).
7
Figure 4 The example forms used for testing.
8
A
Directory
Micros
Soco
Tafe
Westp
B
Number of files
100
6
100
50
C
= B*F
Size of all the tiff files (bytes)
2,141,539
141,673
3,640,274
4,456,211
D=
G*B+H
Size of the compressed file(bytes)
467,548
25,702
274,344
896,958
E
= C/D
Average compression rate over tiff
4.58
5.51
13.27
4.97
F
= C/B
Average size of
each tiff file (bytes)
21,415
23,612
36,403
89,953
G=
(D-H)/B
Average size of each compressed
image(bytes)
4,497
1,652
2,389
16,490
H
Size of the template (bytes)
17,832
15,792
35,462
72,646
Table 1 Form Document Compression Experiment Results.
5. Results
6. Discussion and Conclusion
More than 370 forms of 16 different types are used to test
the proposed TEM compression method. All the forms are
used in banks, companies or government organizations.
Most forms are filled in by hand and some filled-in
patterns touch or cross form frames or preprinted patterns.
The forms are scanned at a resolution of 200 dpi with a
small skew, less than 5, and saved as TIFF files with
CCITT Group 4 format. The compressed images are also
saved as TIFF files with CCITT Group 4 format.
As is well known, the redundancy in document image
representation can be classified into two categories: local
and global. The global redundancy can be further
classified as pattern redundancy in a same image and
pattern assemblage redundancy in similar images. The
proposed compression method reduces the redundancy in
both categories. The TEM method extracts a template for
representing the patterns that are similar in a set of form
document images so as to reduce the pattern assemblage
redundancy in similar images. The binary image
compression and facsimile transmission standards
mentioned in Section 1 provide the method to reduce
local redundancy. CCITT Group 4 is used in this scheme.
A better compression result can be achieved if the JBIG
standard is employed. It is possible to reduce the pattern
redundancy in a same image, which is minimal in the case
of form document compression, by another method. We
intend to address this in our future study.
The forms used for testing have a variety of styles and
formats. Figure 4 presents some examples. Some forms
contain black backgrounds with white characters and
some have halftone patterns. The font sizes in the
examples are also different.
The proposed scheme for multi-copy form document
compression is coded in C++ and run on a PC with a
Pentium II 350 MHz CPU and 64 MB RAM. The typical
image size of the forms for testing is about 1600 x 2200.
On average it takes 90 seconds for template extraction
when 6 filled-in forms are used, 16 seconds for
compression and 3 seconds for decompression of each
image. As the code used for testing was not speed
optimized, the processing speed can be improved further.
The compression rates for different kinds of form images
are listed in the Table 1. The compression rates vary over
a large range from about 4.5 to over 13, which are main
depending on the size of the filled-in data and the quality
of the form images.
From another viewpoint, data compression may be
classified as lossless and lossy compression. For the
lossless compression, there is no partial reduction on data
while it is being performed. An exact copy of the original
image can be completely recovered. For the lossy
compression, some irrelevant data will be discarded
during the compression and the recovered image is only
an approximated version of the original image. Lossy
compression itself can be divided into two categories,
resolution deduction and no-resolution deduction. The
multi-copy form document compression scheme
9
introduced in this paper is a no-resolution deduction lossy
compression, which reduces global redundancy with the
proposed TEM strategy and reduces local redundancy
with a lossless compression technique. This compression
scheme allows lossy compression at much higher
compression ratios than the lossless ratios of the existing
standards with almost invisible degradation of quality.
The experiment results showed the efficiency of the
proposed method.
A statistical template extraction algorithm is developed
using greyscale images by overlapping a number of
binary images. Form images de-skewing, location and
distortion adjusting are employed in the scheme to realize
the TES strategy for practical application. The proposed
prototype matching and substitution method can deal with
all the three possible situations and is effective to reduce
global redundancy.
References
1.
A. Antonacopoulos, Page segmentation using the
description of the background, Computer Vision and Image
Understanding, June 1998, Vol. 70, No. 3, pp. 350-369.
2.
B. Yu and A. K. Jian, A Generic System for Form Dropout,
IEEE Transaction on Pattern Analysis and Machine
Intelligence, November 1996, Vol. 18, No. 11, pp. 11271134.
3.
O. E. Kia, D. S. Doermann, A. Rosenfeld and R. Chellapa,
Symbolic Compression and Processing of Document
Images, Computer Vision and Image Understanding, June
1998, Vol. 70, No. 3, pp. 335-349.
4.
H. Yan, Skew Correction of Document Images Using
Interline Cross-Correlation, CVGIP: Graphical Models and
Image Processing, November 1993, Vol. 55, No. 6, pp.
538-543.
5.
D. Bodson, S. Urban, A. Deutermann and C. Clarke,
Measurement of data compression in advanced group 4
facsimale, system, Proc. IEEE 73, 1985, 731-739.
6.
A. Dengel, ANASTASIL: A system for low-level
geometric analysis of printed documents, in Structured
Document Image Analysis (H. S. Baird, H. Bunke, and Y.
Yamamoto, Eds.), pp.70-98, Springer-Verlag, Berlin, 1992.
7.
S. N. Srihari and V. Govindaraju, Analysis of textual
images using the Hough transform, Mach. Vision Appl. 2,
1989, 141-153.
8.
T. Akiyama and N. Hagita, Automate entry system for
printed documents, Patt. Recogn., Vol. 23, No. 11, pp.
1141-1154, 1990.
9.
H. Bunke, P.S.P. Wang (Eds), Handbook of character
recognition and document image analysis, Publisher
Singapore, World Scientific, c1997. Pp. 19-22.
10. M. Holt and C. Xydeas, Recent developments in image
data compression for digital facsimile, ICL Tech. J. 1986,
123-146.
11. S. Inglis and I. Witten, Compression-based template
matching, in Proceedings of the IEEE Data Compression
Conference, 1994.
12. Jianguo Wang and Hong Yan, Mending Broken Handwriting
with a Macrostructure Analysis Method to Improve
Recognition, Pattern Recognition Letters, 20 (1999) 855864.
10