INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.4, No.8, Jul. 2012

Offline Handwritten Devanagari Script Recognition

Full Text (PDF, 340KB), PP.37-42


Views:140   Downloads:4

Author(s)

Ved Prakash Agnihotri

Index Terms

Handwritten Character Recognition, Image Processing, Feature Extraction, Chromosome Bit String

Abstract

Handwritten Devanagari script recognition system using neural network is presented in this paper. Diagonal based feature extraction is used for extracting features of the handwritten Devanagari script. After that these feature of each character image is converted into chromosome bit string of length 378. More than 1000 sample is used for training and testing purpose in this proposed work. It is attempted to use the power of genetic algorithm to recognize the character. In step-I preprocessing on the character image, then image suitable for feature extraction as here is used. Diagonal based feature extraction method to extract 54 features to each character. In the next step character recognize image in which extracted feature in converted into Chromosome bit string of size 378. In recognition step using fitness function in which find the Chromosome difference between unknown character and Chromosome which are store in data base.

Cite This Paper

Ved Prakash Agnihotri, "Offline Handwritten Devanagari Script Recognition", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.8, pp.37-42, 2012. DOI: 10.5815/ijitcs.2012.08.04

Reference

[1]S. Mori, C.Y. Suen and K. Kamamoto,“Historical review of OCR research and development,” Proc. Of IEEE, vol. 80, pp. 1029-1058, July 1992.

[2]S. Impedovo, L. Ottaviano and S. Occhinegro,“Optical character recognition”, International Journal Pattern Recognition and Artificial Intelligence, vol. 5(1-2), pp. 1-24, 1991.

[3]V. K. Govindan and A.P. Shivaprasad,“Character Recognition-A review,” Pattern Recognition, vol. 23, no. 7, pp. 671-683, 1990.

[4]R. Plamondon and S. N. Srihari,“On-line and Off-line handwritten character recognition: A comprehensive survey,” IEEE Transactions o Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63-84, 2000. 

[5]N. Arica and F. Yarman-Vural,”An Overview of Character Recognition Focused on Off-line Handwriting”,IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2001, 31(2), pp. 216-233.

[6]U. Bhattacharya, and B. B. Chaudhuri,”Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals,”IEEE Transaction on Pattern analysis and machine intelligence, vol. 31, no. 3, pp. 444-457, 2009.

[7]U. Pal, T. Wakabayashi and F. Kimura,”Handwritten numeral recognition of six popular scripts,” Ninth International conference on Document Analysis and Recognition ICDAR 07, vol. 2, pp. 749-753,2007.

[8]R. G. Casey and E. Lecolinet,”A Survey of Methods and Strategies in Character Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligance, vol. 18, no. 7, pp. 690-706, july 1996.

[9]Anil K. Jain and Torfinn Taxt,”Feature extraction methods for character recognition-A Survey,” Pattern Recognition, vol. 29, no. 4, pp. 641-662, 1996.

[10]R. G. Casey and E. Lecolinet,”A Survey of Methods

[11]C. L. Liu, H. Fujisawa,”Classification and learning for character recognition: Comparsion of methods and remaining problems,” International Workshop on Neural Networks and Learning in Document Analysis and Recognition, Seoul, 2005.

[12]F. Bortolozzi, A. S. Brito, Luiz S. Oliveira and M. Morita,”Recent advances in handwritten recognition,” Document Analysis, Umapada Pal, Swapan K. Parui, Bidyut B. Chaudhuri, pp. 1-13.

[13]Rafael C. Gonzalez, Richard E. woods and Steven L. Eddins, Digital Image Processing using MATLAB, Pearson Education, Dorling Kindersley, South Asia, 2004.

[14]S. V. Rajashekararadhya, and P. Vanajaranjan,” Efiiciency zone based feature extraction algorithm for handwritten numeral recognition of four popular south-Indian scripts,” Journal of Theoretical and Applied Information Technology, JATIT, vol. 4, no. 12, pp. 1171-1181,2008.