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ASME Press Select Proceedings

International Conference on Mechanical and Electrical Technology, 3rd, (ICMET-China 2011), Volumes 1–3

By
Yi Xie
Yi Xie
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ISBN:
9780791859810
No. of Pages:
906
Publisher:
ASME Press
Publication date:
2011

Automatic recognition of handwritten numerals has importance in practical fields. In this communication we propose an efficient automatic recognition system for isolated handwritten Latin and Devanagari numerals. Fourier descriptors based features are extracted and are input to a feed forward backpropagation neural network for classification. The numeral recognition is also done by template matching classifier based on correlation metric. Using total 10000 training samples the proposed technique is tested on total 2360 handwritten Latin and Devanagari numerals extracted from dates present on bank cheques. The average recognition accuracy of 98.42% and 99.63% are obtained by using artificial neural network (ANN) classifier and template matching (TM) classifier respectively.

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