STRUCTURAL FEATURES FOR RECOGNITION OF HAND WRITTEN KANNADA CHARACTER BASED ON SVM

International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 5,No.2, April 2015
DOI : 10.5121/ijcseit.2015.5203 25
STRUCTURAL FEATURES FOR RECOGNITION
OF HAND WRITTEN KANNADA CHARACTER
BASED ON SVM
S.A.Angadi a
and Sharanabasavaraj.H.Angadi b
a
Department of Computer Science and Engineering, Visvesvaraya Technological
University,Belgavi, Karnataka,India
b
Department of Computer Science and Engineering, Rural Engineering College,Hulkoti
Karnataka,India.
ABSTRACT
Research in image processing involves many active areas, of these Recognition of Handwritten character
holds lots of promises and is challenging one .The idea is to enable the computer to be able to recognize
intelligibly hand written inputs In this paper, a new method that uses structural features and support
vector Machine (SVM) classifier for recognition of Handwritten Kannada characters is presented. On an
average recognition accuracy of 89.84 % and 85.14% for handwritten Kannada vowels and Consonants
obtained with this proposed method, inspite of inherent variations.
KEYWORDS—Handwritten Character recognition (HCR), Kannada script, preprocessing, feature
extraction, SVM Classifier.
1. Introduction
Character recognition is an important subtask of document image processing [1, 2]. It involves
identifying the various characters that make up the text of the document. The field of character
recognition has seen many reported works, most of which are on English and other foreign
languages. It has been noticed that optical Character Recognition finds application in various
areas and many researchers have shown interest in finding better accuracy of character
recognition on foreign languages, but to the some extent researchers are working on Indian
languages and finds difficulties on these Indian script because of multi lingual, hence there is very
less work been seen in Kannada language, which means to say there's a scope of work to be done.
One of challenging field is hand written Character Recognition within the applications of
character recognition. Hand written information used as an important mode of communication
between the people, since the beginning of mankind and will continue through many ages.
Therefore handwritten recognition [3, 4] plays an important role in the fields of pattern
recognition .Typical pattern recognition system operates in two phases, the very first is Training
(learning) and second one is Testing (Recognition). In first phase that’s training method system
learns from large number of patterns of which the classes are known: in the recognition phase the
system is required to classify patterns for which the classes are unknown. First method does
consist of image preprocessing, feature extraction and feature storage. In the second i.e.

26
recognition phase preprocessing of unknown image is occurred then its features are obtained and
compared to those learned in the training phase that’s first phase.
Enormous work has been recently noted down on the development of handwritten character
recognizers for English, Arabic, Chinese and other scripts. However, there is very less work has
been noted down on developing OCR system especially for Indian languages like Tamil, Telugu,
Gurumukhi, Oriya , because of India is a multi-lingual and multi script country. However, to the
best of our knowledge very little work has been carried out with respect to Kannada language.
Due to the impact and the advancement in the information Technology , more emphasis is being
given in Karnataka to the use of Kannada at all levels and hence the use of Kannada in a
computer system is also a necessity Therefore , efficient OCR system for Kannada are need of the
day.
The proposed method works in two phases. The first phase(Training phase), preprocessing
operations like resizing of image is done so as to make the recognition process independent of
size then the normalization of the image is carried out to extract the structural features like
perimeter, area, eccentricity etc from the set of samples and support vector machine is trained.
During the second phase (testing phase) the set of samples are preprocessed and the structural
features are obtained. The trained feature set along with the testing features is given to the
recognition module which employs support vector machine classifier to recognize the characters.
The method has been evaluated on hand written Kannada vowels and consonants collected from
students of schools and colleges. These images are scanned by the flatbed scanner using 300 dpi.
The system has given a recognition accuracy of 89.84 % and 85.14% for vowels and consonants
respectively.
The remaining part of the paper is organized as follows Section 2 presents a survey of literature in
recognition of Handwritten Indian language text. Section 3 describes Kannada character set and
database. Section 4 describes the method for recognition of hand written Kannada character.
Section 5 presents experimentation and Section 6 gives conclusions.
2. Literature Review
Handwriting recognition (or HWR) is the ability of a computer to receive and interpret intelligible
handwritten input from sources such as paper documents, photographs, touch-screens and other
devices. Recognition of handwritten characters by a computer is a difficult problem due to the
human handwriting variability, uneven skew, orientation writing habit, style. A few such
recognition algorithms are described in the following
Amitabh Wahi, [5] et.al, presented a paper on Handwritten Tamil Character Recognition using
Moments. Zernike moments and Legendre Polynomial features are used in the pattern recognition
to extract the features of Tamil characters. Neural classifier has been used for the classification
purpose. Handwritten Bangla characters features are extracted by local chain-code histograms
using MLP classifier proposed by Bhattacharya et al [6] . Das et al [7] have extracted features
based on quad tree, shadow, and longest run. And using these features multi-layer perceptron
(MLP) and support vector machine (SVM) classifiers recognizes different groups of characters.
Thechallenges involved in the identification of scripts particularly on handwritten documents are
reviewed in [8]. Here described the challenges involved in script identification, the applications of
script identification. Ashwin et al [9] have formed three basic Zones for the underlying character

27
image Support vector machines are employed for the classification of characters and have
achieved an accuracy of 86.11%.
The Gaussian filters are used to down sample each segmented block to extract directional features
for Kannada,Tamil and Telugu hand written characters recognition with aid of quadratic classifier
is presentedin [10]. In [11] FLD based unconstrained handwritten Kannada character recognition
by using Euclidean distance measure is described and the mean recognition accuracy of 68% is
reported. Shreya N. Patankar et.al[12] have proposed a method that aims at recognizing Marathi
language -Barakhadi characters by recognizing a vowel and a consonant separately using
Invariant moment features with quadratic classifier. Recognition of isolated handwritten Kannada
vowels is proposed in [13].The Invariant moments are used as features and K-NN classifier is
used for classification. The recognition results for vowels are 85% on average. Mamatha et.al [14]
describesatechnique to remove the noise induced in the handwritten Kannada documents. Tamil
handwritten recognition system [15] having different font size and type extracts features from
Zernike moments and Legendre Polynomial which have been used in pattern recognition with aid
of Neural classifiers. Using Support Vector Machine (SVM) an attempt is made to recognize the
similar looking Bangla basic characters, numerals and vowel modifiers [16]. From the literature
survey, it is evident that there is still lot of on scope for research in handwritten Kannada
character recognition.
In this paper, a SVM based approach for handwritten character recognition using structural
features is proposed. As an initial attempt, the work is restricted to isolated and constrained
vowels and consonant characters rather than considering entire character set.
3. Kannada Character Set and Database
Kannada script consists of 49 basic characters which are grouped into swaragalu
(vowels),Vyanjanagalu-consonants and Yogavahakagalu..i.e, The Kannada script also has 10
numerals from 0 to 9 Kannada handwriting recognition is challenging task due to large character
set, complex shape presence of compound characters and modifiers and similarity between
characters etc. There is no standard dataset of handwritten Kannada texts available, hence we
have collected handwritten text and built our own dataset for characters. These datasets were
collected from students of primary schools and engineering college. For this purpose every
individual was asked to write vowels and consonants on prescribed forms. The forms were
scanned at 300 dpi. Asgray scale images, using a flatbed HP scanner. The characters were then
manually extracted from the scanned Images. For each of the 49 symbols we selected 50 samples
are used. In total there are 2490 samples in the whole dataset for training and testing. The detailed
description of the feature extraction and classification of proposed methodology is given in the
following section.
4. Proposed Methodology
Structural and topological features used by the proposed method for recognition of hand written
Kannada vowels and consonants. Figure 3. Illustrates block schematic of the proposed method for
recognition of handwritten Kannada character under which major steps included are
preprocessing, feature extraction, knowledge base for training and classifier. SVM consists of
training module (SVM_train) and classification module (SVM_test).The proposed methodology
works as follows;

28
• Collect the data samples
• Scan with 300 dpi and store it as bmp format
• Normalize the image to the size of 30x30 pixels and apply thinning operation on it.
• Extract the structural features and store the features as vector
• Train SVM with the above features. The support vectors are stored as models
• Test the SVM with the features of unknown characters.
The detailed explanation of each stage is given in the following
Image acquisition
In image acquisition, the recognition system acquires a scanned image as an input in bmp format
using flatbed scanner. The acquired image is given to the preprocessing phase.
Pre Processing
The images acquired may contain skewness, noises etc, which leads to the miss classification of
characters. In order to reduce these factors, preprocessing is very essential.
Figure 3.Proposed flow diagram of Handwritten Character Recognition System
The goal of preprocessing is to increase the quality of hand written data. The preprocessing stage
performs size normalization, bounding box generation and further the thinning operation. The
sequences of pre-processing steps are described below.

29
Normalization
It is the process of converting the random sized image into standard sized image. For this purpose
the character images are resized to 30x 30 pixel size. The normalized image is subjected to the
thinning process.
Thinning
To reduce the storage space and processing time thinning is carried out, without distorting shape.
The thinning process is as follows afterapplying bounding box, it extracts the shape information
of the character. Further thinning is carried out on the image which is cropped and the
morphological operator is employed for the purpose. The thinned image is further processed for
feature extraction.
Feature Extraction
The feature extraction [17, 18, 19, 20, 21, 22, 23, 24] stage captures the distinct characteristics of
the digitized character for recognition. The main goal of feature extraction process is to find
unique patterns in image discriminating pattern classes of images. In this phase for each
character, a structural feature vector is extracted and it comprises of
The following structural/topological features. In this stage, the structural features are extracted
from the images using regionprop( ) and are stored into a feature vector F as
F= [ StructFeatures ] StructFeature=[SFi] 1=<i<=43 Where SFi is the structural features of the
character images. The dataset from the images are then applied to the training phase using
svmtrain() .The training process trains images with the given structural feature vectors. These
structural features are used by the classifier to categorize the character image.
Classification
Support vector machine (SVM) classifiers [25, 26, 27, 28, 29, 30, and 31] have gained
prominence in the field of pattern recognition/classification. The SVM process generates the
support vectors on margins on which data points lies. SVM approximates the function using the
following form
f(x) = sgn( w . Ф(x) + b ) (1)
Where, w is the weight vector, b is a bias and Ф(x) represents a high-dimensional feature space
which is nonlinearly mapped from the input space x. The coefficients w and b are estimated by
minimizing the regularized risk function. The ONA approach is used in the proposed method for
decomposition of the classification problem from N class pattern recognition into several two
class classification problems. The explanation using the above mentioned techniques for Kannada
character recognition is described in the following section.

30
5. Experimentation
The proposed methodology has been evaluated for hand written Kannada vowels and consonants
which are collected from different persons. These images are having uneven thickness and other
degradations. Therefore a bounding box is fit to extract the exact character image and the image is
then resized to 30x30 pixel sizes and the resulting image is thinned.
Further the structural/topological features of the character like orientation, Filled Area, Perimeter,
Eccentricity, EquiviDiameter, Convex Area and so on are extracted .
The recognition results are also brought out in Figure 5 and Figure 6 for the vowel and
consonants. The overall recognition rates of vowels is 89.84 % and consonant is 85.14% The
method is implemented on Pentium Processor T4300(2.1 GHz,800MHzFSB) System with 3GB
RAM with Mat lab 7.8.0 and results as shown are satisfactory.
Figure 5. Vowel recognition performance
Figure 6. Consonant recognition performance
6. Conclusion
For hand written Kannada vowels and consonants recognition the proposed method is evaluated
using structural features. The proposed method is capable of recognizing isolated and constrained
handwritten Kannada vowels and consonants. The recognition system has training and testing
phase. The characters are written from different persons are scanned using a flatbed scanner with
300 dpi. The images have uneven thickness and other degradations. Therefore preprocessing

31
operations is performed by bounding fitting, resizing the image to 30x30 and thinning of the
image, so on. Further the structural features of the character are explored for construction of
knowledge base. The test characters are categorized into vowel/consonant classes using the
multiclass SVM classifier, obtaining 89.84 % efficiency in recognizing Kannada vowels
(swaragalu) and 85.14% efficient in recognizing consonants. There is a scope for improving the
methodology by using better features, which will be explored, in future works.
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