OCR for Script Identification of Hindi (Devnagari) Numerals using Feature Sub Selection by Means of End-Point with Neuro-Memetic Model

Authors: Banashree N. P., R. Vasanta

Abstract:

Recognition of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character depending on the type of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent. Our work focused on a technique in feature extraction i.e. global based approach using end-points information, which is extracted from images of isolated numerals. These feature vectors are fed to neuro-memetic model [18] that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. . In proposed scheme data sets are fed to neuro-memetic algorithm, which identifies the rule with highest fitness value of nearly 100 % & template associates with this rule is nothing but identified numerals. Experimentation result shows that recognition rate is 92-97 % compared to other models.

Keywords: OCR, Global Feature, End-Points, Neuro-Memetic model.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084782

References:

[1] Hussain & Kabuka M.R, "A novel feature recognition Neural network & its application to character recognition", IEEE TRANS, PAML, Volume 16, 1994, pp-98-106.
[2] B.Yegnanarayan, Artificial Neural Network, Prentice Hall of India Private limited, New Delhi, fourth edition, 2001.
[3] H.K. Lam, S.H. Ling, F.H.F Leung, and P.K.S. Tam Tuning of the structure and Parameters of neural network using an improved genetic algorithm, in Proc. 27th Annual Conf. of the IEEE Industrial Electronics Society (IECON 01), Denver, Colorado, 29 Nov.-2 Dec. 2001, pp. 25- 30.
[4] N. J. Radcliffe, and P. D. Surry "Formal Memetic Algorithms," Evolutionary Computing, Springer- Verlag, Berlin, 1994 pp, 1-16.
[5] Jacek, M. Zurada, "Introduction to Artificial Neural Network System" Jaico Publishing house, third edition 1999.
[6] M.Hanamandhu, M Vamshi Krishna & P.Chaitanya Kumar, "Some approach to recognition of handwritten Numeral" National Conference on document analysis& recognition, NCDAR, 2001, pp 35-38.
[7] Alessandro Vinciarelli(2002,A survey on offline cursive word recognition pattern recognition , 35:1433.
[8] Ahmed,S.M,et.al ,(nov.1995)Experiments in character recognition to develop tools for an optical character recognition system,IEEE inc.1st National multi topic conf proc.nust ,rawalpindi, pakistan,pp 61-67.
[9] Alexandre lemieux,christian gagne & marc Parizeau (2002).Genetically Engineering of Handwriting Representations proc. of the international workshop on frontiers in handwriting Recognition (IWFHR), Nigagaraon- lake.August 6-8.
[10] Bortolozzi, F., Britto Jr, .A Oliveira, L.S and Morita, M., (2005, recent Advances in handwriting Recognition. in umapada pal et al editors, Documnet Analysis, 1-31.
[11] Gader P.D., Forester B., Ganzberger M., A. Bilies, B Mitchell, M .halen, T.Youeum (1991).Recognition of handwritten digits using template & model matching.Pattern recognition, 5(24):421-431.
[12] Govinda, V.K & Shivaprasad, A P (1990), Character recognition-a review, Pattern recognition .23:671-683.
[13] Hebert Jean-François, Parizeau Marc & Nadia Ghazali(1998).A new fuzzy geometric representation for on-line isolated character recognition.proc of 14th international conference on Pattern recognition,Brisbane:1121-1123.
[14] Mantas, J (1986), An overview of character recognition Methodologies, Pattern recognition, 19(1986)425-430.
[15] Suen, C.Y .,Berthod, M., & Mori,S (1980), Automatic Recognition of hand printed character-the state of atr , proceeding of IEEE.68(1980) 469-487.
[16] Nouboud,F., & Plamondon,(1990).Online Recognition of hand printed chracter:survey and beta tests, pattern recognition,23:1031-1044.
[17] Plamondon Rejean & sargur N, Srihari, (2000), on line & off line handwriting recognition: A comprehensive survey IEEE Transactions on PAMI.22 (1):63-84.
[18] Prof K.A Sumithra Devi, N.P Banashree & Dr Annamma Abraham, "An Evolutionary time-series Model For partitioning a Circuit pertaining to VLSI Design using Neuro-memetic Algorithm ",in proc. 4th IASTED International Conference on Circuit, signal & system, San Francisco,USA , 20 Nov.-22 Nov .2006,pp 325-329.