A generalized knowledge-based system for the recognition of unconstrained handwritten numerals

A method of recognizing unconstrained handwritten numerals using a knowledge base is proposed. Features are collected from a training set and stored in a knowledge base that is used in the recognition stage. Recognition is accomplished by either an inference process or a structural method. The scheme is general, flexible, and applicable to different methods of feature extraction and recognition. By changing the acceptance parameters, a continuous range of performance can be achieved. Encouraging results on nearly 17000 totally unconstrained handwritten numerals are presented. The performance of the system under different recognition-rejection tradeoff ratios is analyzed in detail