In this paper, A dynamic handwritten Chinese signature verflcation system based upon a Bayesian neural network is presented. Due to a great deal of variability of handwritten Chinese signatures, the proposed Bayesian neural network is trained by an incrementaZ Zearning vector quantization (ILVQ) algorithm, which endows this system with incremental learning ability, and outputs a posteriori probability to give a more reliable distance estimation. The performance analysis was based upon a set of signature data consisting of 800 true specimens, 200 "simple " forgeries and 200 "skilled" forgeries. The experimental results show the type I error is about 2% and the type II error rates are about 0.1% and 2.5% for %nple" and "skilled" forgeries respectively.
The stroke analysis method is an effective approach for handwritten Chinese character recognition. But as we know, it is very difficult to accurately extract the strokes. In this paper, a robust stroke extraction method is proposed. First, smoothing and thinning processes are applied to smooth the shape and to obtain the skeleton of the observed character. Then the end point, internal point and fork point are detected by calculating their own crossing numbers while the corner points are determined by a knowledge-based iterative method. Virtual-end-points are introduced for separating a stroke into a certain number of line segments without losing the connection relations among them. By representing each line segment as a vertex and the connection relation of two segments as an edge, the observed character can be represented by an attributed graph. Finally, a stroke extraction procedure is proposed to extract the strokes from the global structures of the character. After each stroke of a character is extracted, the cross points can also be determined. Experimental results have shown that the proposed method is more effective than the other methods.3,5−6
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