Offline recognition of handwritten Bangla characters: an efficient two-stage approach

The present work deals with recognition of handwritten characters of Bangla, a major script of the Indian sub-continent. The main contributions presented here are (a) generation of a database of handwritten basic characters of Bangla and (b) development of a handwritten character recognition scheme...

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Bibliographic Details
Published in:Pattern analysis and applications : PAA 2012-11, Vol.15 (4), p.445-458
Main Authors: Bhattacharya, U., Shridhar, M., Parui, S. K., Sen, P. K., Chaudhuri, B. B.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer Science
Computer science
control theory
systems
Exact sciences and technology
Industrial and Commercial Application
Pattern Recognition
Pattern recognition. Digital image processing. Computational geometry
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Summary:The present work deals with recognition of handwritten characters of Bangla, a major script of the Indian sub-continent. The main contributions presented here are (a) generation of a database of handwritten basic characters of Bangla and (b) development of a handwritten character recognition scheme suitable for scripts like Bangla consisting of many similar shaped characters for the benchmark results. The present database is a pioneering development in the context of recognition of off-line handwritten characters of this script. It has 37,858 handwritten samples and accommodates a large spectrum of handwriting style by Bangla speaking population. This database will be made available ( http://www.isical.ac.in/~ujjwal/download/Banglabasiccharacter.html ) free of cost to researchers for further studies. Also, we identified two major factors affecting high recognition accuracies for the present character samples, namely, (a) erratic nature of the presence of headline (shapes of Bangla characters usually contain a horizontal line in its upper part) and (b) existence of several pairs of similar shaped characters. The proposed recognition approach takes care of the above factors. It identifies any confusion in the first stage classification between a pair of similar shaped character classes and resolves the same in the second stage classification by extracting a feature vector based on a non-uniform grid.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-012-0278-6