HANDWRITTEN GURMUKHI CHARACTER RECOGNITION : M.Tech. Thesis

HANDWRITTEN GURMUKHI CHARACTER RECOGNITION

A Thesis
Submitted in Partial Fulfillment of the
Requirements for the Award of the Degree of

MASTER OF TECHNOLOGY

in

COMPUTER SCIENCE & ENGINEERING

By
Kartar Singh Siddharth
(Roll No. 09203008)

Under the Supervision of

Dr Renu Dhir     Associate Professor

And

Mrs. Rajneesh Rani Assistant Professor

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

DR B R AMBEDKAR NATIONAL INSTITUTE OF TECHNOLOGY
JALANDHAR

July 2011

Download the thesis report in pdf format:
Handwritten Gurmukhi Character Recognition: An M.Tech. Thesis Report

Handwritten Gurmukhi Character Recognition Using Zoning Density and Background Directional Distribution Features

This is my first research paper published in May-June issue of IJCSIT (International Journal of Computer Science and Information Technologies). IJCSIT Vol 2 Issue 3

Handwritten Gurumukhi Character Recognition Using Zoning Density and Background Directional Distribution Features

Abstract:
This paper presents an approach to recognize isolated handwritten Gurumukhi characters. Extracted features used are 16 zonal densities and the 8 background directional distribution features for each of 16 zones. All sample images of Gurumukhi characters used are normalized to 32*32 pixel sizes. Zoning density is computed by dividing number of foreground pixels in each zone by number of total pixels in the zone. Background directional distribution values are calculated for each foreground pixel by directional distribution of its neighbouring background pixels. The value for each directional distribution is computed by summing up the values specified in a mask for corresponding neighbour background pixels. A specific mask is used in particular direction. These feature values for each direction are summed up for all pixels in each zone. Thus adding 16 zoning density features and 128 background directional distribution features (8 features in each of 16 zones) we used feature vector containing 144 features in our experiment. SVM classifier with RBF kernel is used for classification. We have taken 200 samples of each of 35 basic Gurumukhi characters in our experiment, which are collected from 20 different writers each contributing to write 10 samples of each of 35 characters. Thus we have used total 7000 character samples. By training the classifier with whole dataset we obtained 95.04% 5-fold cross validation accuracy. By using dataset taken by 16 writers (size 5600) to train the system and testing the 1400 samples by another 4 unknown writers to classifier, we obtained 89.14% accuracy. By splitting the handwritten samples of each writer to train and test the system in the ratio of 4:1, we received 99.93% test accuracy for known writers. Thus we got 94.53% average test accuracy for known and unknown writers and 95.04% 5-fold cross validation accuracy.

Authors:
Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani

Keywords:
Isolated Handwritten Gurumukhi Character Recognition, Zoning Density, Background Directional Distribution, SVM Classifier.

Citation:
Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani, “Handwritten Gurumukhi Charater Recognition Using Zoning Density and Background Directional Features”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2, Issue 3, pp. 1036-1041, May-June 2011.

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