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Proceedings Paper

Using morphology and associative memories to associate salt-and-pepper noise with OCR error rates in document images
Author(s): Vicente P. Concepcion; Matthew P. Grzech
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Paper Abstract

For each scanned text image, we generate the morphological pattern spectrum which captures image object shape and size information. We use the spectrum to characterize the noise content of a text document image by considering only the region of the spectrum near the origin. Noise is known to affect many image processing operations and we chose to consider optical character recognition (OCR) in this experiment. We associate noise that is characterized by a partial pattern spectrum with OCR performance as measured 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 noise.

Paper Details

Date Published: 1 August 1992
PDF: 9 pages
Proc. SPIE 1661, Machine Vision Applications in Character Recognition and Industrial Inspection, (1 August 1992); doi: 10.1117/12.130270
Show Author Affiliations
Vicente P. Concepcion, MITRE Corp. (United States)
Matthew P. Grzech, MITRE Corp. (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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