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Proceedings Paper

Character segmentation and thresholding in low-contrast scene images
Author(s): Lowell LeRoy Winger; M. Ed Jernigan; John A. Robinson
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Paper Abstract

We are developing a portable text-to-speech system for the vision impaired. The input image is acquired with a lightweight CCD camera that may be poorly focused and aimed, and perhaps taken under inadequate and uneven illumination. We therefore require efficient and effective thresholding and segmentation methods which are robust with respect to character contrast, font, size, and format. In this paper, we present a fast thresholding scheme which combines a local variance measure with a logical stroke-width method. An efficient post- thresholding segmentation scheme utilizing Fisher's linear discriminant to distinguish noise and character components functions as an effective pre-processing step for the application of commercial segmentation and character recognition methods. The performance of this fast new method compared favorably with other methods for the extraction of characters from uncontrolled illumination, omnifont scene images. We demonstrate the suitability of this method for use in an automated portable reader through a software implementation running on a laptop 486 computer in our prototype device.

Paper Details

Date Published: 7 March 1996
PDF: 11 pages
Proc. SPIE 2660, Document Recognition III, (7 March 1996); doi: 10.1117/12.234710
Show Author Affiliations
Lowell LeRoy Winger, Univ. of Waterloo (Canada)
M. Ed Jernigan, Univ. of Waterloo (Canada)
John A. Robinson, Univ. of Waterloo (Canada)

Published in SPIE Proceedings Vol. 2660:
Document Recognition III
Luc M. Vincent; Jonathan J. Hull, Editor(s)

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