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

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.