Automatic recognition of handwritten characters via feature extraction and multi-level decision

Abstract

Automatic recognition of handwritten alphanumeric characters is designed by making use of topological feature extraction and multi-level decision making. By properly specifying a set of easily detectable topological features, it is possible to convert automatically the handwritten characters into stylized forms and to classify them into primary categories. Each category contains one or several character pattern classes with similar topological configurations. Final recognition is accomplished by a secondary stage which performs local analysis on the characters in each primary category. The recognition system consists of two stages, global recognition followed by local recognition. Automatic character stylization results in pattern clustering which simplifies the classification tasks considerably, while allowing a high degree of generality in the acceptable writing format. Simulation of this scheme on a digital computer has shown only 2% misrecognition.