A fuzzy graph theoretic approach to recognize the totally unconstrained handwritten numerals

Abstract An automatic off-line character recognition system for totally unconstrained handwritten numerals is presented. The system was trained and tested on the field data collected by the U.S. Postal Services Department from dead letter envelopes. It was trained on 1763 unnormalized samples. The training process produced a feasible set of 105 Fuzzy Constrained Character Graph Models (FCCGMs). FCCGMs tolerate large variability in size, shape and writing style. Characters were recognized by applying a set of rules to match a character tree representation to a FCCGM. A character tree is obtained by first converting the character skeleton into an approximate polygon and then transforming the polygon into a tree structure suitable for recognition purposes. The system was tested on (not including the training set) 1812 unnormalized samples and it proved to be powerful in recognition rate and tolerance to multi-writer, multi-pen, multi-textured paper, and multi-color ink. Reliability, recognition, substitution error, and rejection rates of the system are 97.1, 90.7, 2.9, and 6.4%, respectively.