A multi-layer classifier for recognition of unconstrained handwritten numerals

Abstract

A hierarchical architecture for recognition of the unconstrained handwritten numerals is proposed. In the first stage of preclassification, a set of structural features named four-zone codes is adopted to preclassify the numerals. Due to the large degree of data and distortion of characters, it is possible to classify two different numerals with same features into a class. A secondary preclassification that utilizes topological stroke features is presented to solve this ambiguity. In order to promote the recognition rate to be a practical OCR system, a three layer Bayesian neural network with 20 dimensional global feature vectors is designed for fine classification of the confusing classes. Experimental results show that the recognition rate of the proposed hierarchical OCR system for handwritten numerals is over 99.82% based on 15423 samples.