基于特征融合的脱机中文笔迹鉴别

Writer Identification of Offline Chinese Handwriting Documents Based on Feature Fusion

提出一种基于文本依存笔迹特征融合的文本独立特征构造方法.建立基于方向指数直方图法笔迹特征(文本依存特征)的两因子分解模型.笔迹特征可分解成字符因子和书写因子两部分.通过两因子方差分析与数据挖掘,分离出与字符无关的书写因子,得到基于文本依存方法的文本独立特征.该方法对检材与样本笔迹的字符数量较少,特别是相同字很少或是根本没有相同字的情况下,能取得较理想的笔迹鉴别准确率,为少量字笔迹鉴别提供解决问题的思路.

A text-independent method for writer identification is proposed based on the fusion of text-dependent features. A two-factor model is developed, and thus the feature of handwriting is decomposed into the character factor and the writing factor. According to the deviation analysis of the two factors and data mining, the writing factor is separated, the text-independent feature based on text-dependent method is extracted, and the classifier is developed. The proposed method is effective for the samples with few characters, especially for the samples without the same characters between the unknown script and the corresponding one in the database. The proposed method provides a new way for writer identification with few characters in the writing sample.