This document summarizes research on recognizing handwritten characters in the Odia language. It discusses two main approaches to Odia character recognition: template matching and feature extraction. The document also reviews several papers on Odia handwritten character recognition, describing the different techniques used, such as neural networks, genetic algorithms, and rule-based methods. Overall, the document surveys existing work on developing systems for Odia optical character recognition (OCR) and handwritten character recognition.

1. International Journal of Advanced Research in Technology, Engineering and Science (A Bimonthly Open
Access Online Journal) Volume1, Issue2, Sept-Oct, 2014 ISSN: 2349-7173(Online)
A Survey on Odia Handwritten Character
Recognition
Dhabal Prasad Sethi
_____________________________________________
ABSTARCT
In Image Processing, recognition of handwritten
document or printed document is the challenging field.
Optical character recognition is a type of document
image analysis where scanned copies of machine
printed or hand written documents are taken as input
and the OCR software system translate it into an
editable readable digital text. In this article I surveys
the different work have done about odia optical
/handwritten character recognition.
_____________________________________________
Keywords: OCR, Odia OCR, Odia handwritten
character recognition, Feature Extraction,
Binarization, Inversion, Skeletonization.
____________________________________________
I.INTRODUCTION
Optical character recognition is a process which
converts handwritten or typed text in to an electronics
format which can be stored, interpreted and processed
by a computer. Odia hand written document can be
recognized as two methods.
1) Matrix matching/Template Matching
2) Feature extraction
Template matching is the simplest method for
classification. In template matching, each individual
image pixels are used as features. Classification is done
by comparing an input character image with a set of
templates (or prototypes) from each character class
.Each comparison results in a similarity measure
between the input character and the template.
In feature extraction methods, unique features of each
character are taken .The features are taken based on
vertical, horizational, right diagonals, left diagonal line
segment of the character. The figure below shows the
preprocessing of hand written character recognition.
_____________________________________________
First Author Name: Dhabal Prasad Sethi, Lecturer in CSE,
Govt. College of Engineering, Keonjhar,
Odisha.,Email:excellent11@yahoo.in
_____________________________________________
(Figure1 shows the OCR system works)
a)The preprocessing step consist of Binarization,
Inversion, Skeletonization.The binarization of image
consist of four types of images .a) gray scale images)
binary images) indexed images, 4) RGB images.
The input images are taken is called as RGB images.
The image having values in the range of [0,255] and [0,
65535] respectively is called gray scale images. The
input image which is converted into black and white
images with a particular thresholding value is called
binary images. Binary images are a combination of 0s
and 1s.The brightness which is the average of the values
of red, green and blue is found for each pixel is then
compared with a threshold value. The pixels which have
values less than the threshold value are set to 0 and the
pixels which values are greater than threshold values are
set to 1.The threshold value may predetermined or can
be calculated from the image histogram.
(Figure2 shows Steps to binarize the image)
Inversion is the processes by which white pixels are
converted into dark and dark pixels are converted into
white. After binarization each image contains black
pixels with white background. We know white pixels
have the value1 and black pixel have 0.
Skeletonization: The image obtained after inversion is
skeletonised, where the foreground pixels are removed
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2. International Journal of Advanced Research in Technology, Engineering and Science (A Bimonthly Open
Access Online Journal) Volume1, Issue2, Sept-Oct, 2014 ISSN: 2349-7173(Online)
preserving the extent and connectivity of the original
region. It is useful because it provides a simple and
compact representation of the shape of the image
b) In Feature extraction each character is represented
as a feature vector, which is the identity of that
character. The aim of the feature extraction is to extract
a set of features, which maximizes the recognition rate
with the least amount of elements.
c) Classification: To classify the character various
algorithms and methods can be used. The classification
is done by various factors of the real world problem
.Examples of the classifiers are hidden Markova model,
Support vector machine, Artificial neural networks.
II. LITERATURE SURVEY
N Tripathy and U Pal[2]presented a paper named “
Handwriting segmentation of unconstrained Oriya text
“.In this paper they propose a water reservoir concept
based scheme for segmentation of unconstrained odia
handwritten text into individual character. From
experiments they have observed that the proposed
“touching character” segmentation module has
96.7%accuracy for two-character touching strings.
Debananda Padhi[6] presented a paper named “novel
hybrid approach for odia handwritten character
recognition system”. His proposed system is based on
the algorithm of feed forward back propagated neural
network combined with genetic algorithm to perform the
optimum feature extraction and recognition. The hand
written odia characters are classified according to
similarity of their shapes and features from the data set
collected of different person’s handwritten notes using
ANN. He proposed to use five ANN are feed into the
GA which chooses the fittest and the best solution and
provided us the recognition alphabets.
Soumya Mishra, Debasish Nanda and Sanghamitra
Mohanty[1] presented a paper named “odia character
recognition using neural network.”They have used the
back propagation Neural network for efficient
recognition where the errors were corrected through
back propagation and rectified neuron values were
transmitted by feed-forward method in the neural
network of multiple layers, e.g the input layer, the
output layer and the middle layer or hidden layer.
Debasish Basa, Sukadev Meher[3] presented a paper
named “Handwritten odia character recognition”. In this
work they have proposed robust structural solution for
odia character recognition where a given text is
segmented into lines and then each line is segmented
into individual words and then each word is segmented
into individual characters or basic symbols. Basic
symbols are the identified as the fundamental units of
segmentation used for recognition. Using unique
structure of some characters they have got better results
compared to other.
Pradeepta Kumar Sarangi, P Ahmed [7] presented a
paper named “recognition of Handwritten Odia
Numerals using Artificial Intelligence Technique”. In
this paper they implemented artificial neural network
architecture with back propagation learning for
recognition of isolated handwritten odia numerals. The
recognition accuracy of test patterns was 85.30% and
misclassification was 14.70%.The high percentage of
misclassification could be due to the local minima
problem of back propagation which indicates that some
other technologies like better training algorithm or
hybridization with genetic algorithm should be adapted
to overcome the local minima problem.
Bhagirathi Kumar [5]and his team presented a paper
named Optical Character Recognition using Ant Miner
Algorithm: A case study on odia character recognition.
In this Paper they have used the Ant-miner algorithm
(AMA) for offline OCR of handwritten odia scripts. The
AMA is a rule-based approach. The rules are
incrementally tuned during the training.
III.CONCLUSION
In this article we studied the different methods used for
recognition of handwritten documents along with the
different work have done by different people for odia
hand written character recognition.
REFERENCES
[1]Oriya Character Recognition using Neural Networks by Soumya
Mishara,Debasish Nanda,Sanghamitra Mohanty from P.G department
of CSA,Utkal University.
[2]Handwriting Segmentation of Unconstrained Oriya text by
N.Tripathy and U pal from Computer Vision and Pattern Recognition
unit, Indian statistical institute,203,BTRoad,kolkota
[3] Handwritten Odia Character Recognition by Debasish Basa,
Sukadev Meher from Department of Electronics and Communication
Engineering, NIT, Rourkela, India at National Conference on Recent
Advances in microwave tubes,March,2011
[4]Recognition of Isolated Handwritten Oriya Numerals using
Hopfield Neural Networks by Pradeepta Kumar Sarangi, Ashok
Kumar Sahoo, P Ahmed at international journal of computer
application,Feb2012,page36-42
[5]Optical Character Recognition using ant miner Algorithm:A case
study on Oriya Character Recognition by Bhagirathi kumar,Niraj
Kumar,chaubalata Palai,Pradeep kumar jena,subhagata chsttsopsdhya
at international journal of computer applications,jan,2013 page17-22
[6] Novel Hybrid Approach for Odia Handwritten Character
Recognition by Debananda Padhi from P.G Department of Computer
Science and Application, Utkal University at International Journal of
Advanced
Research in Computer Science and Software Engineering.page150-157
[7] Recognition of Handwritten Odia Numerals using Artificial
Intelligence Technique by Pradeepta Kumar Sarangi and P Ahmed at
International Journal of Computer Science and Application,page41-48
[8]Hand written odia character recognition by Anuraag hota,
Souramya pradhan department of electronics and communication
engineering, national institute of technology, Rourkela
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