Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques
ACM Transactions on Asian Language Information Processing (TALIP)
The study of different similarity measure techniques in recognition of handwritten characters
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Proceeding of the workshop on Document Analysis and Recognition
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In this paper we present a system towards the recognition of off-line Oriya handwritten characters. Since most of the Oriya characters have curve-like stroke, we use curvature feature for the recognition purpose. To get the feature, at first, the input image is size normalized and segmented into 49脳49 blocks. Curvature is then computed using bi-quadratic interpolation method and quantized into 3 levels according to concave, linear and convex regions. Next direction of gradient is quantized into 32 levels with /16 intervals, and strength of the gradient is accumulated in each of the 32 directions and in each of the 3 curvature levels of every block. A spatial resolution is made to get 7脳7 blocks from 49脳49 blocks and a directional resolution is made to get 8 directions from 32 directions. Using curvature features for 3 levels we get 1176 (7脳7 blocks 脳 8 directions 脳 3 levels) dimensional features. Finally using principal component analysis we reduce the dimension 1176 to 392 and this 392 dimensional feature vector is fed to a quadratic classifier for recognition. We tested 18190 samples of Oriya handwritten samples and obtained 94.60% accuracy from our proposed system.