基于笔画分析和背景细化的粘连手写汉字切分

离线手写汉字的切分是识别的前提，其中粘连手写汉字的切分最为困难。提出一种基于笔画分析和背景细化的粘连手写汉字的切分新方法。对粘连字符图像作细化处理，检测端点、叉点和角点等特征点，根据特征点提取笔段。按笔段的长度、相互之间的位置关系以及投影信息确定切分点。细化粘连字符的背景图像，从切分点出发在细化的背景中选取分割路径，实现粘连手写汉字的切分。实验表明，本方法对于粘连手写汉字具有令人满意的切分效果。

Segmentation of Connected Handwritten Chinese Characters Based on Stroke Analysis and Background Thinning

Segmentation of off-line handwritten Chinese characters is the premise of recognition. It is most difficult to segment connected characters. A novel algorithm based on stroke analysis and background thinning was proposed to segment connected handwritten Chinese characters. The feature points, viz. end points, fork points and corner points are detected in the thinned image of connected characters. The segments between the feature points arc considered as substrokcs and are extracted. The information of the length of substrokes, the topological relations and projection is employed to locate connected points. By thinning the background, suitable separation path is determined. The experimental results show that satisfactory performance is achieved by the presented method for segmentation of connected handwritten Chinese characters.