Wavelet Packet Transform and Neuro-Fuzzy Approach to Handwritten
Character Recognition
Abstract
This paper presents a novel method for automatic handwritten character
recognition by combining wavelet packet transform with neuro-fuzzy
approach. The time-frequency localization and compression capability
of wavelet packet transform using best-basis algorithm is used for
feature extraction, enhancing the accuracy of recognition at pixel
level. The best-basis algorithm automatically adapts the transform to
best match the characteristics of the signal, minimizing the additive
cost function. Since fuzzy sets and fuzzy logic remains as a means
for representing, manipulating and utilizing uncertain information and
to provide a framework for handling uncertainties and imprecision
associated with real world problems, a fuzzy logic system is used for
classification purpose. A neural network system is used for
recognition purposes since they provide computational power, fault
tolerance, and learning capability to the systems. Characteristic
features are extracted by taking wavelet packet transform using
best-basis algorithm and are given as input to the fuzzy classifier
where they are fuzzified and classified using IF ... THEN rules, and
given to a neural network recognition system. This method is more
efficient for handwritten character recognition as well as personal
identification compared to energy sorted wavelet transform of
character images, since characters it contain very few edges in the
images. Simulation of characters is done for 3 multiresolution levels
using symmlet and results show that this method is more efficient than
the methods using only fuzzy logic.
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Last updated by Loren Schwiebert Email: 
on Jun-06-2001