Statistical Modeling and Learning for Recognition-Based Handwritten Numeral String Segmentation

Abstract

This paper proposes a recognition based approach to handwritten numeral string segmentation. We consider two classes: numeral strings segmented correctly or not. The feature vectors containing recognition information for numeral strings segmented correctly are assumed to be of the distribution of Gaussian mixture model (GMM). Based on this modeling, the recognition based segmentation is solved under the Max-Min posterior Pseudo-probabilities (MMP) framework of learning Bayesian classifiers. In the training phase, we use the MMP method to learn a posterior pseudo-probability measure function from positive samples and negative samples of numeral strings segmented correctly. In the process of recognition based segmentation, we generate all possible candidate segmentations of an input string through contour and profile analysis, and then compute the posterior pseudo-probabilities of being the numeral string segmented correctly for all the candidate segmentations. The candidate segmentation with the maximum posterior pseudo-probability is taken as the final result. The effectiveness of our approach is demonstrated by the experiments of numeral string segmentation and recognition on the NIST SD19 database.