A unified framework for handwritten character recognition using deformable models
William K. Cheung, Dit Yan Yeung, R.T. Chin · 1995
Recently, some deformable models have been proposed for character recognition, due to their ability to capture variations in handwriting. These proposed systems use deformable models to represent characters and to extract features, and subsequently feed the extracted information into a classifier. They often treat the three components -- modeling, feature extraction, and classification -- as three disjoint and sometimes independent tasks. In this paper, we propose to integrate a deformable model with MacKay's evidence framework [1] as a unified approach to modeling, feature extraction and classification, and to apply this framework to handwritten character recognition. Our proposed system begins with fitting character models to the raw image and ends at the selection of the most probable output class, all using Bayesian inference. Due to the use of deformable models, the system is invariant to shift, rotation and size changes as well as writing variations. In addition, it does not re...