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Proceedings Paper

New approach for segmentation and recognition of handwritten numeral strings
Author(s): Javad Sadri; Ching Y. Suen; Tien D. Bui
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Paper Abstract

In this paper, we propose a new system for segmentation and recognition of unconstrained handwritten numeral strings. The system uses a combination of foreground and background features for segmentation of touching digits. The method introduces new algorithms for traversing the top/bottom-foreground-skeletons of the touched digits, and for finding feature points on these skeletons, and matching them to build all the segmentation paths. For the first time a genetic representation is used to show all the segmentation hypotheses. Our genetic algorithm tries to search and evolve the population of candidate segmentations and finds the one with the highest confidence for its segmentation and recognition. We have also used a new method for feature extraction which lowers the variations in the shapes of the digits, and then a MLP neural network is utilized to produce the labels and confidence values for those digits. The NIST SD19 and CENPARMI databases are used for evaluating the system. Our system can get a correct segmentation-recognition rate of 96.07% with rejection rate of 2.61% which compares favorably with those that exist in the literature.

Paper Details

Date Published: 17 January 2005
PDF: 9 pages
Proc. SPIE 5676, Document Recognition and Retrieval XII, (17 January 2005); doi: 10.1117/12.586046
Show Author Affiliations
Javad Sadri, Concordia Univ. (Canada)
Ching Y. Suen, Concordia Univ. (Canada)
Tien D. Bui, Concordia Univ. (Canada)


Published in SPIE Proceedings Vol. 5676:
Document Recognition and Retrieval XII
Elisa H. Barney Smith; Kazem Taghva, Editor(s)

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