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ASME Press Select Proceedings
International Conference on Mechanical and Electrical Technology, 3rd, (ICMET-China 2011), Volumes 1–3
Yi Xie
Yi Xie
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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.

Key Words
1. Introduction
2. Data Collection And Preprocessing
3. Proposed System
4. Fourier Descriptors Based Feature Extraction
5. ANN Classification
6. Correlation Based Recognition
7. Results
7. Summaries
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