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Two Methods for Recognition of Hand Written Farsi Characters
Mohammad Reza Jenabzadeh, Reza Azmi, Boshra Pishgoo, Samanesadat Shirazi
Pages - 512 - 520     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
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KEYWORDS
Optical Character Recognition, Hand Written Farsi Characters, Neural Networks, Wavelet Transform, Decision Tree
ABSTRACT
Optical character recognition (OCR) is one of the active bases of sample detection topics. The current study focuses on automatic detection and recognition of hand written Farsi characters. For this purpose; we proposed two different methods based on neural networks and a special post processing approach to improve recognition rate of Farsi uppercase letters. In the first method, we extracted wavelet features from borders of character images and learned a neural network based these patterns. In the second method, we divided input characters into five groups according to the number of their components and used a set of appropriate moment features in each group and classified characters by the Bayesian rule. In a post-processing stage, some structural and statistical features were employed by a decision tree classifier to reduce the misrecognition rate. Our experimental results show suitable recognition rate for both methods.
CITED BY (5)  
1 Shayegan, M. A. (2015). Dataset size and dimensionality reduction approaches for handwritten farsi digits and characters recognition (Doctoral dissertation, University of Malaya).
2 Safdar, Q. T. A., & Khan, K. U. (2014, December). Online Urdu Handwritten Character Recognition: Initial Half Form Single Stroke Characters. In Frontiers of Information Technology (FIT), 2014 12th International Conference on (pp. 292-297). IEEE.
3 Kchaou, M. G., Kanoun, S., Slimane, F., & Affes, S. B. Arabic Character Recognition based on Statistical Features.
4 Shayegan, M. A., Aghabozorgi, S., & Raj, R. G. Ensemble of Decision Stumps for Handwritten Farsi/Arabic Digit Recognition.
5 Kholladi, M. M. K., Chikhi, M. S., Mostefai, M. S., Zidani, M. A., & Mazouzi, M. S. Combinaison de classifieurs pour la reconnaissance de mots arabes manuscrits.
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Dr. Mohammad Reza Jenabzadeh
- Iran
jenabzadeh@gmail.com
Dr. Reza Azmi
- Iran
Mr. Boshra Pishgoo
- Iran
Mr. Samanesadat Shirazi
- Iran