Statistical Features for Text-Independent Writer Identification

Abstract

Automatic writer identification is desirable in many important applications including banks, forensics, archeology, and so forth. A key and still open issue in writer identification is how to represent the distinctive and robust features of individual handwriting. This chapter presents three statistical feature models of handwritings in paragraph-level, stroke-level, and point-level, respectively, for text-independent writer identification. The three methods evolve from coarse to fine, showing the technology roadmap of handwriting biometrics. The proposed methods are evaluated on CASIA handwriting databases and perform well in both Chinese and English handwriting datasets. And the experimental results show that fine scale handwriting primitives are advantageous in text-independent writer identification. The best performing method adopts the probability distribution function and the statistical dynamic features of tripoint primitives for handwriting feature representation, achieving 95% writer identification accuracy on CASIA-HandwritingV2 with 1,500 handwritings from more than 250 subjects. And a demo system of online writer identification is developed to demonstrate the potential of current algorithms for real world applications.