Using morphology and associative memories to associate salt-and-pepper noise with OCR error rates in document images

Vicente P. Concepcion; Matthew P. Grzech

doi:10.1117/12.130270

1 August 1992 Using morphology and associative memories to associate salt-and-pepper noise with OCR error rates in document images

Vicente P. Concepcion, Matthew P. Grzech

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Proceedings Volume 1661, Machine Vision Applications in Character Recognition and Industrial Inspection; (1992) https://doi.org/10.1117/12.130270
Event: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, 1992, San Jose, CA, United States

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.

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Vicente P. Concepcion and Matthew P. Grzech "Using morphology and associative memories to associate salt-and-pepper noise with OCR error rates in document images", Proc. SPIE 1661, Machine Vision Applications in Character Recognition and Industrial Inspection, (1 August 1992); https://doi.org/10.1117/12.130270

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KEYWORDS

Optical character recognition

Content addressable memory

Binary data

Image processing

FDA class I medical device development

Detection and tracking algorithms

Distance measurement

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