We propose a new segmenting method for handwritten Chinese
signatures based on the wavelet transform for signature verification.
There are some differences in identifying a handwritten signature
and in recognizing a handwritten character because there are meaningful
features hidden in writing habits when an individual is signing his or
her signature. These features exhibit themselves in the pen-down, penup,
and in the corner of a stroke. Therefore the segmentation for identifying
a signature and for recognizing a character should be different
even though the same characters are involved. We propose to segment
an input signature curve at the inflection points, and we locate the inflection
points by detecting the zero-crossing points of the wavelet transforms
of the input signature. Experimental results show that this new
segmenting method has better segmentation capability than other methods
that are usually used.