Wavelet Packet Transform and Fuzzy Logic Approach For Handwritten Character Recognition


This paper presents a novel method for handwritten character recognition using wavelet packet transform and fuzzy logic. This method exploits the time-frequency localization and compression capability of wavelet packet transform, using the best basis algorithm to enhance the accuracy of recognition at the pixel level and the computational capability of fuzzy logic with linguistic variables, which is a universal approximator if it uses enough rules. The best basis algorithm automatically adapts the transform to best match the characteristics of the signal, as well as minimize the additive cost function. The wavelet packet transform of the handwritten characters are taken using the best basis algorithm. The standard deviation of the spread of the coefficients in each multi-resolution level are computed, which forms the characteristic features for the characters. These features are given as input to the fuzzy logic character recognition system, where these are fuzzified, analyzed, and the corresponding characters are given as output using IF ... THEN rules. This method is more efficient for handwritten character recognition than energy sorted wavelet transform of character images, since it contains only a few edges in the image. Simulation of four multi-resolution levels for each character is done using symmlet and results show that they have better accuracy than the methods using only fuzzy logic.

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Last updated by Loren Schwiebert Email: on Jun-06-2001