Integration of a structural features-based preclassifier and a man-machine interactive classifier for a fast multi-stroke character recognition

Abstract

A transputer-based parallel machine for handwritten character recognition is proposed. An algorithm based on structural features and on a tree classifier was used to accomplish the pre-classification of the unknown sample in order to speed up the recognition process. The algorithm for the final classification is based on the description of the strokes through Fourier descriptors. The learning phase is accomplished through a man-machine interactive process. The proposed system can expand its knowledge base. A special representation of this knowledge base is proposed in order to record a great amount of data in a suitable way. A fast multistroke handwritten isolated character recognition system is presented. The test of this system was performed on a PC based prototype while the realization of a parallel transputer based working machine is in progress. Experimental results obtained applying these machines to handwritten numerals recognition are reported.