Neural Combination of ANN and HMM for Handwritten Devanagari Numeral Recognition

Abstract : In this article, a two-stage classification system for recognition of handwritten Devanagari numerals is presented. A shape feature vector computed from certain directional-view-based strokes of an input character image, has been used by both the HMM and ANN classifiers of the present recognition system. The two sets of posterior probabilities obtained from the outputs of the above two classifiers are combined by using another ANN classifier. Finally, the numeral image is classified according to the maximum score provided by the ANN of the second stage. In the proposed scheme, we achieved 92.83% recognition accuracy on the test set of a recently developed large image database[1] of handwritten isolated numerals of Devanagari, the first and third most popular language and script in India and the world respectively. This recognition result improves the previously reported[2] accuracy of 91.28% on the same data set.
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Communication dans un congrès
Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006
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Dernière modification le : samedi 28 juillet 2018 - 14:54:01
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U. Bhattacharya, S.K. Parui, B. Shaw, K. Bhattacharya. Neural Combination of ANN and HMM for Handwritten Devanagari Numeral Recognition. Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006. 〈inria-00104481〉

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