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A MLP Classifier for Both Printed and Handwritten Bangla Numeral Recognition

  • A. Majumdar
  • B. B. Chaudhuri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)

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.

Keywords

Hide Layer Optical Character Recognition Handwritten Character Scaled Conjugate Gradient Postal Mail 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. Majumdar
    • 1
  • B. B. Chaudhuri
    • 1
  1. 1.Pricewaterhouse Coopers, Pvt. Ltd, India., CVPR UnitIndian Statistical InstituteKolkataIndia

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