Feature relevance analysis for writer identification
Imran Siddiqi, Khurram Khurshid, Nicole Vincent · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
This work presents an analytical study on the relevance of features in an existing framework for writer identification from offline handwritten document images. The identification system comprises a set of 15 features combining the orientation and curvature information in a writing with the well-known codebook based approach. This study aims to find the optimal feature subset to identify the author of a questioned document while maintaining acceptable identification rates. Employing a genetic algorithm with a wrapper method we carry out a feature selection mechanism and identify the most relevant features that characterize the writer of a handwritten document.