ABSTRACT
Since
the late 1960’s, research on recognition of unconstrained handwritten
characters has made impressive progress and many systems have been developed,
particularly in machine printed and on-line character recognition. However,
there is still a significant performance gap between humans and machines in the
recognition of off-line totally unconstrained handwritten character
recognition. Generally,
off-line handwritten character recognition system includes three stages: image
preprocessing, feature extraction, and classification. Preprocessing is
primarily used to reduce noise or variations of handwritten characters. A
feature extraction is essential for data representation and extracting
meaningful features for later processing. A classification stage assigns the
characters to one of the several classes.
Considering the influence on recognition performance, the features
extraction plays a very important role in handwriting recognition. This has led
to the development of a variety of features for handwritten recognition and their
recognition performances. The baseline system used in this work applies a
Global Approach for feature extraction combined with a Local Approach based on
zoning mechanism, and uses Class-Modular architecture feed forward MLP (Multi
Layer Perceptron) in the classification stage.
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