基于多通道分解与匹配的笔迹鉴别研究

笔迹鉴别是通过分析手写字符的书写风格来判断书写人身份的一门技术.笔迹鉴别 的关键步骤是提取反映书写风格的笔迹特征.笔迹特征包括笔划位置、方向、搭配关系等,它 们可以通过图像多通道分解提取和表达出来.本文提出一种用于笔迹鉴别的二值图像多通道 分解方法,利用字符的笔划方向性先进行方向分解,然后对每个方向的子图像进行频带分解. 用分解后的采样信号值作为笔迹特征,用特征匹配方法进行书写人识别,得到了很好的实验 结果.

Writer Identification by Multichannel Decomposition and Matching

By writer identification (WI), the writer of a handwritten document is detected by analyzing the writing style. The crucial stage in WI is the extraction of individual features which reflect the writing style. There are many informative individual features, in which are the stroke position, direction, and the collocation. Some features can be represented simutaneously by multichannel decomposition (MCD) of images. In this paper, a MCD approach of binary images for WI is presented. The image is decomposed into directional subimages according to strokes direction, and then each subimage is further decomposed into several frequency bands. Finally, the sampled values of subband images are used as individual features for WI. Promising results have been achieved in the writer recognition experiments by feature matching.