Method and apparatus for establishing pixel color probabilities

Image analysis – Histogram processing – For setting a threshold

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382 37, G06K 900

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048316571

ABSTRACT:
A method of creating a decision tree to enable a character recognition device to recognize characters in a new unknown font includes scanning a document printed in the unknown font to generate an array of pixels, with each pixel having a neighborhood state based on the colors of the surrounding pixels. A plurality of clusters of pixels, each representing a printed character, are identified. A pixel in one cluster is selected, and its neighborhood state determined. The neighborhood state is used to address a memory that has stored in it a probability table providing the probability, in a second font different from the new font being taught, that a pixel is black as a function of its neighborhood state. The stored probability of black associated with the neighborhood state of the select pixel is read from the memory and assigned as the probability of the selected pixel. The decision tree for the new font is generated using that assigned probability of black.

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Moihuddin et al., "Lossless Binary Image Compression Based on Pattern Matching," International Conference on Computers, Systems & Signal Processing, Bangalore, India (Dec. 9-12, 1984).
Casey and Nagy, "Decision Tree Design Using A Probabilisitic Model," IEEE Transactions on Information Theory, vol. IT-30, No. 1, pp. 93-99, (Jan., 1984).
Casey, "Automatic Generation From OCR Logic From Scanned Characters," IBM Technical Disclosure Bulletin, vol. 22, No. 3, pp. 1189-1190 (Aug., 1979).
Lotspiech, "Probabilistic Character Recognition Decision Trees With Style Variations", IBM Technical Disclosure Bulletin, vol. 22, No. 10, pp. 4420-4422 (Mar., 1980).

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