Cursive handwritten word recognition using multiple segmentation determined by contour analysis

This paper proposes a method for cursive handwritten word recognition. Cursive word recognition generally consists of segmentation of a cursive word, character recognition and word recognition. Traditional approaches detect one candidate of segmentation point between characters, and cut the touching characters at the point [1]. But, it is difficult to detect a correct segmentation point between characters in cursive word, because form of touching characters varies greatly by cases. In this research, we determine multiple candidates as segmentation points between characters. Character recognition and word recognition decide which candidate is the most plausible touching point. As a result of the experiment, at the character recognition stage, recognition rate was 75.7%, while cumulative recognition rate within best three candidates was 93.7%. In word recognition, re4cognition rate was 79.8%, while cumulative recognition rate within best five candidates was 91.7% when lexicon size is 50. The processing speed is about 30 sec/word on SPARC station 5.