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Proceedings Paper

Contrast enhancement of mail piece images
Author(s): Yong-Chul Shin; Ramalingam Sridhar; Victor Demjanenko; Paul W. Palumbo; Jonathan J. Hull
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Paper Abstract

A New approach to contrast enhancement of mail piece images is presented. The contrast enhancement is used as a preprocessing step in the real-time address block location (RT-ABL) system. The RT-ABL system processes a stream of mail piece images and locates destination address blocks. Most of the mail pieces (classified into letters) show high contrast between background and foreground. As an extreme case, however, the seasonal greeting cards usually use colored envelopes which results in reduced contrast osured by an error rate by using a linear distributed associative memory (DAM). The DAM is trained to recognize the spectra of three classes of images: with high, medium, and low OCR error rates. The DAM is not forced to make a classification every time. It is allowed to reject as unknown a spectrum presented that does not closely resemble any that has been stored in the DAM. The DAM was fairly accurate with noisy images but conservative (i.e., rejected several text images as unknowns) when there was little ground and foreground degradations without affecting the nondegraded images. This approach provides local enhancement which adapts to local features. In order to simplify the computation of A and (sigma) , dynamic programming technique is used. Implementation details, performance, and the results on test images are presented in this paper.

Paper Details

Date Published: 1 August 1992
PDF: 11 pages
Proc. SPIE 1661, Machine Vision Applications in Character Recognition and Industrial Inspection, (1 August 1992); doi: 10.1117/12.130271
Show Author Affiliations
Yong-Chul Shin, SUNY/Buffalo (United States)
Ramalingam Sridhar, SUNY/Buffalo (United States)
Victor Demjanenko, SUNY/Buffalo (United States)
Paul W. Palumbo, SUNY/Buffalo (United States)
Jonathan J. Hull, SUNY/Buffalo (United States)

Published in SPIE Proceedings Vol. 1661:
Machine Vision Applications in Character Recognition and Industrial Inspection
Donald P. D'Amato; Wolf-Ekkehard Blanz; Byron E. Dom; Sargur N. Srihari, Editor(s)

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