Abstract
This paper presents a new off-line character recognition system for handwritten Thai alphabets. Unlike many existing techniques, the proposed system has been developed without any assumption on the way that the characters are written. As the results, the reported recognition rate of the proposed system correlates well with the results from real-world tests.The system consists of two classification phases where the techniques that are appropriate for each phase have been independently developed. The first phase classifies character images based on the overall character structure by a technique that emphasizes high tolerance to noise and shape distortion. The second phase performs local contour analysis using a fuzzy system in order to extract detailed features that distinguishes among similar characters. The recognition engine for both phases are neural network-type enhanced with technique called outpost vector for improved generalization. The experimental results on arbitrary handwriting confirms the effectiveness and practicality of the technique.
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