A MLP classifier for both printed and handwritten bangla numeral recognition

Authors:
A. Majumdar;B. B. Chaudhuri
Affiliations:
Pricewaterhouse Coopers, Pvt. Ltd, India., CVPR Unit, Indian Statistical Institute, Kolkata, India;Pricewaterhouse Coopers, Pvt. Ltd, India., CVPR Unit, Indian Statistical Institute, Kolkata, India
Venue:
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Year:
2006

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Abstract

This paper concerns automatic recognition of both printed and handwritten Bangla numerals. Such mixed numerals may appear in documents like application forms, postal mail, bank checks etc. Some pixel-based and shape-based features are chosen for the purpose of recognition. The pixel-based features are normalized pixel density over 4 X 4 blocks in which the numeral bounding-box is partitioned. The shape-based features are normalized position of holes, end-points, intersections and radius of curvature of strokes found in each block. A multi-layer neural network architecture was chosen as classifier of the mixed class of handwritten and printed numerals. For the mixture of twenty three different fonts of printed numerals of various sizes and 10,500 handwritten numerals, an overall recognition accuracy of 97.2% has been achieved.