Writer Identification from Non-uniformly Skewed Handwriting Images

Many techniques have been reported for handwriting-based writer identification. Most such techniques assume that the written text is fixed (e.g., in signature verification). In this paper we attempt to eliminate this assumption by presenting a novel algorithm for automatic text-independent writer identification from non-uniformly skewed handwriting images. Given that the handwriting of different people is often visually distinctive, we take a global approach based on texture analysis, where each writers' handwriting is regarded as a different texture. In principle this allows us to apply any standard texture recognition algorithm for the task (e.g., the multi-channel Gabor filtering technique). Results of 96.0% accuracy on the classification of 150 test documents from 10 writers are very promising. The method is shown to be robust to noise and contents.

• Type: Conference
• Year: 1998
• Where: BMVC
• Authors: H. E. S. Said, G. S. Peake, T. N. Tan, Keith D. Baker
• Keywords: BMVC 1998, Computer Vision, Handwriting-based Writer Identification, Non-uniformly Skewed Handwriting, Writer Identification