基于模糊区域检测的手写汉字笔画提取方法

摘要

针对手写汉字笔画提取的重点和难点--模糊区域的识别和解析问题,提出了一种新的基于模糊区域检测的笔画提取算法.该算法首先利用细化算法提取的fork候选点和fork候选点附近的轮廓信息来检测模糊区域;然后利用图模型来对子笔画和模糊区域进行建模,同时通过构造贝叶斯分类器来分析子笔画对的连续性,并通过路径搜索来得到子笔画序列;最后通过进行B样条插值来提取细化后的笔画.对比实验结果表明,该算法不仅能够有效地用于模糊区域检测和笔画提取,而且能够避免细化结果在模糊区域内的形状畸变.%To effectively identify and interpret ambiguous zones in handwritten Chinese image, a new method for handwritten stroke extraction based on ambiguous-zone detection is proposed. In the method, a candidate set of fork points are extracted using a thinning algorithm, and the ambiguous zones are detected according to the fork points and the contour beside them. Then, the sub-strokes and ambiguous zones can be modeled with a graph, and a Bayesian classifier is built to analyze the continuity of sub-stroke pairs. Finally, sequences of sub-stroke are achieved by searching paths in the graph, and thinned strokes can be retrieved by B-spline interpolation. Experimental results show that the proposed method is effective and accurate for both ambiguous-zone detection and stroke extraction compared to other methods and reduce the shape distortions in ambiguous zones to an acceptable level.