International Journal of Engineering Trends and Technology (IJETT) – Volume22 Number 5- April2015
A Review Paper on Character Recognition
using Binarization technique
Shalu
M.Tech(CSE) Sri Ram College of Engineering, Palwal(India)
Abstract: This paper presents a review on the use of
binarization technique for character recognition. A wellknown document image analysis product is the Optical
Character Recognition (OCR) software that recognizes
characters in a scanned document. The main focus of this
work is to extract features obtained by binarization
technique for recognition of handwritten characters of
English language. Several Preprocessing techniques such as
noise removal, noramalizes image, feature extraction are
used for the preprocessing phase Using Binarization
technique for feature extraction provides very promising
results and the classifier used to recognize the handwritten
characters is the multilayer feed forward neural network.
Keywords: OCR, Binarization technique,
Extraction, Multilayer feed forward network
I.
Feature
B) Character Recognition
INTRODUCTION
The paper is important in our daily life because it is
cheap, reliable, easily available, flexible in filling, secure
for future references and is easy to keep. A huge amount
of important historical data is also written on papers. So,
there is a great demand to digitize all these paper
documents so that the people all over the world can
access these important sources of knowledge. For this
purpose, the image of handwritten text is preprocessed
and segmented into individual characters and are
recognized by a neural network classifier. The process of
reading handwritten text from the static surfaces is
termed as off-line cursive handwriting recognition.
Simulating the behaviour of the human brain into a
machine opened innovative prospects to improve manmachine interface. For the last four decades, the
classification of cursive and unconstrained handwritten
characters has been a major issue in this field of
research.
A) Recognition phases
There are 4 main phases used in the given work. First is
conversion of RGB image into Grayscale image. Then
features extraction is performed and binary image is
obtained then using binarization technique and finally
recognition of characters is performed.
ISSN: 2231-5381
Figure 1: Recognition Phases
Optical character recognition (OCR) is commonly used
term for Character Recognition which is used for the
conversion of digital or handwritten images into
computer readable form. It is a field of research in
pattern recognition, artificial intelligence and machine
vision. Though academic research in the field continues,
the focus on OCR has shifted to implementation of
proven techniques. Optical character recognition (using
optical techniques such as mirrors and lenses) and digital
character recognition (using scanners and computer
algorithms) were originally considered separate fields.
Because very few applications survive that use true
optical techniques, the OCR term has now been
broadened to include digital image processing as well.
Optical Character Recognition (OCR) deals with
machine recognition of characters present in an input
image obtained using scanning operation. The input
document is read preprocessed, feature extracted and
recognized and the recognized text is displayed in a
picture box.
The goal of Optical Character Recognition (OCR) is to
classify optical patterns (often contained in a digital
image) corresponding to alphanumeric or other
characters. The process of OCR involves several steps
including segmentation, feature extraction, and
classification.
In case of academic system and library management, the
significance of the OCR recognition is proven already.
The character recognition is basically performed using
the mirrors or the lenses. The character recognition is
considered as the separate field so that he recognition of
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International Journal of Engineering Trends and Technology (IJETT) – Volume22 Number 5- April2015
characters will be done effectively. OCR is defined as
character recognition was done by U. Pal[2]. In this
the important image processing application in which the
work author presented analysis on 12 classifiers with 4
recognition is based on multiple parameters. These
feature sets. These feature sets includes the projection
parameters includes the feature extraction and feature
distance analysis, subspace method, linear discriminant
specification. The features depends on the algorithmic
function etc. Author performed the analytical study
approaches adapted to extract the features.
under different information analysis such as survature
based and the gradient information analysis.
David Andre[3] presented a work on rule updation based
C) Applications of Character Recognition
on learning approach in OCR system using Genetic
approach. Author defined the genetic programming
There are various areas in which Character Recognition
approach for effective character identification. Author
is doing wonders. Character Recognition approach is
defined a human hand coded rules for initial population
widely used and applied in different fields. Some of the
generation and rule updation. Author analyze the work
fields that uses this approach are Data Entry, Form
on different datasets under different real time problems
Readers, Aid for Blind, Text Entry etc.
such as noise etc.
Angelo Marcelli[4] presented a work based on structural
Process automation is an area of application to
analysis to perform the shape based recognition to apply
control some particular process. The general
the effective encoding and transformation so that the
approach is to get all the available information and
effective vector space will be generated and processed
for the redundancy check use the postcode.
under genetic approach. The vector based structural
Signature Verification and Identification is an area
analysis is performed under genetic approach to perform
useful for banking purpose. The identity of the
the recognition.
writer is established without reading the
Soumen Bag[5] presented a work of recognition of hand
handwriting. And the pattern to be matched is
written character that was based on character structural
simply a signature with signatures collected in
shape. Skeletal Convexity are used to describe the shape
database.
of the character. Recognition is done by using Longest
Common Subsequence matching. The dataset of
Automatic Cartography is helpful for recognizing
handwritten Bengali Character has been taken for the test
characters from maps. The graphics and symbols get
and the promising preliminary results were obtained.
mixed and the different fonts and styles can be
Another work on feature extraction based character
present during recognition.
recognition was done using neural network. This work
was done by J. Pradeep[6] in year 2011. Author defined
Automatic Number Plate Readers basically for
the analysis under different feature extraction approaches
vehicles. Here the input image must be captured by
followed by neural network. This approach includes the
a fast camera and it is not like other bilevel images
effective character training process so that the
and this thing makes recognition complex.
recognition reate will be improved. Author defined the
work for English alphabets.
II.
EXISTING WORK
In Year 2011, Huiqin Lin[7] has defined a research to
improve the assignment system using the character
Lot of work is already done by different researchers to
recognition based recognition system. The presented
improve recognition of characters and make it more
approach was the effective distribution based
effective. Some of the efforts of earlier researchers are
segmentation model in which the segmentation was done
discussed in this section.
using centroid based analysis and the angular analysis as
done to perform the recognition process. Author
Depeng Tao[1]. This work includes is performed using
performed the work on deflection based adapted
the locality alignment approach for discriminative
correction so that high accuracy based matching will be
Chinese characters. The work is performed using a
performed. Author defined the work to improve the
hybrid learning approach performed using locality
recognition rate with the concept of tinning and the
alignment and subspace analysis. The defined approach
regularity analysis between the character positions. This
was kernel based approach that used the PCA as the
kind of analysis includes the skelton based recognition
initial stage and followed by discriminative locality
process.
analysis approach. The obtained results shows that the
In Year 2013, Cao Xinyan[8] performed a work,"
proposed approach had provided the effective accuracy.
Handwritten Mathematical Symbol Recognition Based
Another comparative study on different recognition and
on Niche Genetic Algorithm". This method makes great
classification approaches for handwritten devnagri
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International Journal of Engineering Trends and Technology (IJETT) – Volume22 Number 5- April2015
use of the searching ability of ecological niche genetic
A) Preprocessing
algorithm and the nonlinear mapping and associative
ability of BP neural network, it extracts the coarse grid
Preprocessing of an image is done to remove variability
characteristics, the projector features, cross-cut
in handwritten characters. In this phase, Grayscale
characteristics and structural features , then makes use of
conversion and Binarization is performed.
the operation of choice , cross, variation and obsolete of
the ecological niche genetic algorithm, optimizes the
A.1. GrayScale Conversion
initial weight values and threshold of BP neural network,
finally, makes the well-trained NGA-BP network
In this phase of Preprocessing , the input image of
recognize the mathematical symbols.
handwritten character in .bmp or .jpg image format is
In Year 2010, Reza Azmi[9] performed a work," A
converted to grayscale format by using a MATLAB
hybrid GA and SA algorithms for feature selection in
function ‘rgb2gray’.
recognition of hand-printed Farsi characters". In this
research a hybrid feature selection technique based on
A.2. Binarization
genetic and simulated annealing algorithms is proposed.
this approach is evaluated by using Bayesian classifier
The goal of binarization is to minimize the unwanted
on a dataset of hand-printed Farsi characters.
information present in the image while protecting the
Another work on tamil character recognition using
useful information. It must preserve the maximum useful
neural network was proposed by P. Banumathi[10] in
information and details present in the image, and on the
year 2011. Author defined the process under different
other hand, it must eliminate the background noise
styles, shapes, sizes and orientation. Author work on the
associated with the image in an efficient way.
paragraphs and separate them by using the segmentation
approach by using the concept of centroid based analysis
with special dot based segmentation approach.
Anshul Gupta[11], presented an offline recognition of
handwritten English words. Two classes holistic and
segmentation based is categorized for the recognition. In
holistic, feature extraction is done according to the size
of the vocabulary. Segmentation used bottom up
approaches eventually producing a meaningful text.
Finally, the Postprocessing stage uses lexicon to increase
the accuracy in recognition.
Jia Zeng[12] has defined a character modelling and
recognition approach based on the statistical structured
analysis and markov model based recognition process.
This structured analysis includes the stroke analysis,
Figure 2: Stepwise Input Image Conversions into
neighbourhood character analysis and the encoding
Different formats
technique so that effective recognition will be
B) Feature Extraction
performed. The recognization process is performed on
the certain features using the markov model based
The curvic feature extraction will be performed to
predictive approach. The work is implemented on
identify the image features. These features set will be
Korean dataset and the obtained results shows that the
used as the basic training set for the segmentation and
work has improved the accuracy of the system.
classification process.
III. PROPOSED WORK AND IMPLEMENTATION
Image Acquisition is performed in which handwritten
character images are captured using digital camera or
can also b scanned using scanner. All the characters are
converted to image format such as .jpg or .bmp. These
samples can be written with different colored pens. In
the studied work, samples are contributed by 10 different
people and completely 1300 character image samples are
collected
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Figure 3: Extracted features of image in Binary format
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International Journal of Engineering Trends and Technology (IJETT) – Volume22 Number 5- April2015
References
C) Implementation
The implementation is performed using Neural Network
Training tool (nntraintool).
Figure 4: nntraintool
The network learning iterations must be selected in such
a way that the network may converge properly with least
generalization error. The maximum allowedepochs for
the training process has been set to 100000. If the
network could not converge within the maximum
allowed epochs count, the training will stop.
IV.
CONCLUSION
[1] Dapeng Tao, Similar handwritten Chinese character
recognition by kernel discriminative locality alignment,
Pattern Recognition Letters, pp 186–194, 2014
[2] U. Pal, Comparative Study of Devnagari Handwritten
Character Recognition using Different Feature and
Classifiers, 10th International Conference on Document
Analysis and Recognition, pp 1112-1115, 2009
[3] David Andre," Learning and Upgrading Rules for an OCR
System Using Genetic Programming", 0-7803-18994194@1994 IEEE
[4] Angelo Marcelli," Exploring genetic programming for
modeling character shape", 0-7803-6583-6/00@2000
IEEE
[5] Soumen Bag, Recognition of Bengali Handwritten
Characters Using Skeletal Convexity and Dynamic
Programming, Second International Conference on
Emerging Applications of Information Technology, pp
265-268, 2011
[6] J. Pradeep, Neural Network based Handwritten Character
Recognition system without feature extraction,
International Conference on Computer, Communication
and Electrical Technology – ICCCET, pp 40-44, 2011
[7] Huiqin Lin, The Research of Algorithm for Handwritten
Character Recognition in Correcting Assignment System,
Sixth International Conference on Image and Graphics, pp
456-460, 2011
[8] Cao Xinyan, Handwritten Mathematical Symbol
Recognition Based on Niche Genetic Algorithm, 2013
Third International Conference on Intelligent System
Design and Engineering Applications 978-0-7695-49231/12 © 2012 IEEE
[9] Reza Azmi, A hybrid GA and SA algorithms for feature
selection in recognition of hand-printed Farsi characters,
978-1-4244-6585-9/10©2010 IEEE
[10] P. Banumathi, Handwritten Tamil Character Recognition
using Artificial Neural Networks, International
Conference on Process Automation, Control and
Computing, pp 1-5, 2011
[11] Anshul Gupta, Offline Handwritten Character Recognition
Using Neural Network, 978-1-4577-2058-1@2011 IEEE
[12] Jia Zeng, Markov Random Field-Based Statistical
Character Structure Modeling for Handwritten Chinese
Character Recognition, IEEE Transactions On Pattern
Analysis And Machine Intelligence, Vol. 30, No. 5, pp
767-780, 2008
In the given work, the neural network has been trained
by each of 26 characters 50 times i.e. 1300( 50x26)
character image samples from the database has been
evolved. As a result, an outstanding classification
accurace of 85.62 has been achieved. The techniques like
Training , feature extraction, and classifier for deciding
the accuracy of recognition system can be refined
because there is always a scope of improvement.
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