Wholistic recognition of handwriting using structural features
Nasser Sherkat · 1999
This paper presents the research carried out in producing a wholistic recognizer for static cursive handwritten words. Two sets of handwritten data samples are collected. 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. A number of wholistic features namely, vertical bars, holes and cups are employed. A series of tests are carried out and the results are presented. Using a 200 word lexicon the wholistic recognizer 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.