Wavelet Packet Transform and Fuzzy Logic Approach For Handwritten
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