Optical Character Recognition for Hindi Language Using a Neural-network Approach

Divakar Yadav, Sonia Sánchez-Cuadrado and Jorge Morato
Volume: 9, No: 1, Page: 117 ~ 140, Year: 2013
10.3745/JIPS.2013.9.1.117
Keywords: OCR, Pre-processing, Segmentation, Feature Vector, Classification, Artificial Neural Network (ANN)
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Abstract
Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document"'"s textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

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Cite this article
IEEE Style
D. Yadav and S. S. J. Morato, "Optical Character Recognition for Hindi Language Using a Neural-network Approach," Journal of Information Processing Systems, vol. 9, no. 1, pp. 117~140, 2013. DOI: 10.3745/JIPS.2013.9.1.117.

ACM Style
Divakar Yadav, Sonia Sánchez-Cuadrado and Jorge Morato. 2013. Optical Character Recognition for Hindi Language Using a Neural-network Approach, Journal of Information Processing Systems, 9, 1, (2013), 117~140. DOI: 10.3745/JIPS.2013.9.1.117.