International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 7 - May 2014
Handwriting Verification : An Approach to
Verify Handwriting Using Feature Extraction of
Orientation and Curvature
V Swetha D. Rao #1 , Rashmi Welekar *2
1
2
M.Tech Scholar, CSE Department, SRCOEM, Nagpur, India
Assistant Professor , CSE Department, SRCOEM, Nagpur, India
Abstract—The
writer identification task lies in the
definition of a feature space common to all the
handwritten documents. In this study we have extended
this principle to the whole document database Identifying
handwritten documents is a herculuies task. This paper
consists approach used to identify an individual writer’s
handwriting by extracting features of orientation ,
curvature and width of characters. It is the approach to
study individual’s characteristics . In this approach we
train the classifier with sample words from an writer .
Classifier identifies the trainned samples and displays
writer’s name present in database otherwise it displays
that writer’s name who writing stlye matches to the
sample.
by segmenting the word into those subunits, and
then identifies the units. As an example to identify
any word using this approach the letters are to be
identified first then they are used for the total word
recognition. Holistic approach: It treats the word as
a single, indivisible entity and attempts to recognize
it using features of the word as a whole. So, in this
approach the whole word image features are used
for the recognition purposes.
III. RELATED WORK
Researchers have utilized many different
approaches for both the segmentation and
Keywords— knn classifier, orientation, curvature, feature recognition tasks of word recognition. Some
extraction.
researchers have used conventional, heuristic
techniques for both character segmentation and
I. INTRODUCTION
Optical character recognition (OCR), is a recognition some have used a convex hull based
program that translates scanned or printed image and recursive contour following algorithms, while
document into a text document. Once it is others have used heuristic techniques for
translated into text, it can be stored in ASCII or segmentation followed by ANN based methods for
UNICODE format. There are several applications the character/word recognition process.Hidden
with OCR. Some of the practical applications are as Markov Model based techniques are also used
reading aid for the blind, automatic text entry into widely for both offline and online hand-written
the computer for desktop publication, library document recognition.
cataloging, ledgering, etc. automatic reading for
IV. PROPOSED METHOD
sorting of postal mail, bank cheques and other The method proposed covers basically five phases
documents, document data compression: from image acquisition, pre processing, segmentation,
document image to ASCII format, language feature extraction and classification.
processing such as indexing, spell checking,
grammar checking , multi-media system design.
A. Image Acquisition
II. CONCEPT OVERVIEW
In image scanning recognition system acquires a
There can be mainly two approaches for the word
scanned
image as an input image through a digital
recognition purpose for any handwritten documents.
scanner
or
any other suitable digital input device.
Analytical approach: It treats words as a collection
The
input
captured
may be in gray, color or binary
of simpler subunits such as characters and proceeds
ISSN: 2231-5381
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International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 7 - May 2014
from scanner or digital camera. This image should E. Classification
have a specific format such as JPEG, BMP etc.
Image is classified using features that are
extracted
ie writers are identified in the database.
B. Pre Processing
As for the known writer we know all features that is
The output of the image acquisition is fed as
slant, orientation along with area used by writer. As
input to the preprocessing step. The raw data of
for unknown writer , firstly when image is acquired
handwritten characters will be subjected to a
it processed and features are extracted. Features
number of preprocessing steps to make it useable
extracted are matched with database enteries and
for the next steps. The preprocessing phase aims to
accordingly near matching writer’s name is popped
extract the relevant textual parts and prepares them
up in menu.
for segmentation and recognition. Pre-processing of
the image means applying a number of procedures
for thresholding, filtering, and normalizing so that
final classification can be made simple and more
accurate.
C. Segmentation
An image is present in the form of sequence of
characters that is decomposed into sub-images of
individual character in the segmentation stage. In
this system, labeling process is used for
segmentation of pre-processed input image into
isolated characters by assigning a number to each
character. This labeling provides information about
number of characters in the image.
D. Feature Extraction
Each character has its own differential features,
which play an important role in pattern recognition.
Feature extraction describes the relevant shape
information contained in a pattern so that the task of
classifying the pattern is made easy by a formal
procedure. The main goal of feature extraction is to
obtain the most relevant information from the
original data and represent that information in a
lower dimensionality space. When the input data to
an algorithm is too large and also may be redundant
(much data, but not much information) then the
input data will be transformed into a reduced
representation set of features (also named features
vector). A term feature extraction is termed that
transforms the input data into the set of features.
The features set will be used to extract the relevant
information from the input data in order to perform
the desired task using this reduced representation
instead of the full size input.
ISSN: 2231-5381
Fig. 1 Flowchart showing the proposed system
V. PROPOSED APPROACH
In our approach writer is asked to write statement.
First word is segmented from the statement and into
characters.
Fig.2 Word “ The “ is written by writer (trained sample)
When this word is analyed through the classifier
features are extracted like curvature, orientation and
area along with writer’s name and stored in
database.
Fig.3 Word “Hello” is written by writer(untrained sample)
When classifier checks features of new word to that
of existing word if writer is known (already in
database) then name is popped up otherwise near to
match writer’s name is popped up.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 7 - May 2014
REFERENCES
[1]
[2]
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Fig.4 Menu window showing writer’s name
[4]
VI. CONCLUSIONS
In our proposed method we are able to identify
known writer from trained sample as from
untrained samples, similarly we are also able
identify near about matching of handwriting from
unknown writer.
[5]
[6]
[7]
ACKNOWLEDGMENT
The writers V. Swetha D. Rao and Rashmi
Welekar would like to thank all staff members and
authorities of Shri Ramdeobaba College Of
Engineering & Management Nagpur, India for their
support and motivation.
[8]
[9]
[10]
ISSN: 2231-5381
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V. Swetha D. Rao and Rashmi Welekar, “Survey on Existing
Techniques for Writer Verification”, COMPUSOFT , An
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