Whole word recognition in facsimile images

This paper presents the research carried out in producing a whole recognizor for cursive handwritten words in facsimile images. Two sets of handwritten data samples are collected and converted into facsimile images. The first set comprises approximately 1600 word images from 8 writers and is used for development purposes. The second set consists of approximately 2000 word images from 10 writers. This set is used for testing only. The algorithms for extraction of holistic features namely, vertical bars, holes and cups used in the recognizor are described. A series of test are carried out and the results are presented using a 200 word lexicon. The holistic recognizor produced 62% top rank and 82% in top 5 alternatives. When a lexicon of 1000 words was used these values reduced to 49% and 70% respectively. The future directions of the research for improvement of recognition rate are proposed. It is envisaged that definition of further features would improve the overall accuracy