|
Handwritten Chinese character recognition is known to be a very
difficult problem because of their large vocabulary set of
complex structures and writing variations. In this thesis, a
hierarchical architecture is constructed to recognize
handwritten Chinese characters. First, the preprocessing
operations including smoothing, stroke fitting,
skeletonization, feature point detection are performed to
obtain a suitable data format for further processes. Then a
stroke extraction technique without any operations to correct
the distorted skeleton before extracting process is proposed to
extract all strokes represented by a sequence of line segments.
Based on the fact that the cross point is formed by one stroke
running through another, the cross point can be easily detected
by checking four adjacency line segments that form exact two
strokes. Because the number of Chinese characters is very
large, preclassification stage is usually needed for this work
to reduce the number of candidates in matching process. The
characters with the same number of stroke and cross point are
grouped together in preclassification stage. Finally, for each
group, a fuzzy neural network is proposed to resolve individual
identification. A fuzzy neural network which possesses the
advantages of fuzzy concepts and neural network is also
presented for handwritten Chinese character recognition based
upon the feature of stroke position, direction and length. The
experimental results show that the proposed approach is
effective.
|