DocumentCode :
3320612
Title :
Off-line cursive handwriting segmentation
Author :
Han, Ke ; Sethi, Ishwar K.
Author_Institution :
Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA
Volume :
2
fYear :
1995
fDate :
14-16 Aug 1995
Firstpage :
894
Abstract :
The segmentation of off-line cursive handwriting is an important step in cursive handwriting recognition. In this paper, a new approach is described for this task. The suggested approach uses a set of heuristic rules to determine possible letter boundaries in the image of a cursive word. The heuristic rules are based on associations that exist between certain geometric and topologic features and the English language characters. A segmentation system incorporating the proposed approach has been built to perform segmentation on postal address images. The system includes several preprocessing steps to extract cursive handwritten words from a postal envelope and normalization steps to allow variations in pen thickness and tilt in writing. The experimental results obtained thus far show that the proposed approach is capable of accurately locating the letter boundaries in cursive words
Keywords :
handwriting recognition; image segmentation; optical character recognition; English language characters; cursive handwriting recognition; heuristic rules; letter boundaries; off-line cursive handwriting segmentation; topologic features; Bars; Character recognition; Computer science; Handwriting recognition; Image segmentation; Iterative methods; Laboratories; Natural languages; Neural networks; Writing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-8186-7128-9
Type :
conf
DOI :
10.1109/ICDAR.1995.602045
Filename :
602045
Link To Document :
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