PAPERS: Document Image AnalysisNo Access

DOCUMENT GRAY-SCALE REDUCTION USING A NEURO-FUZZY TECHNIQUE

NIKOS PAPAMARKOS

Division of Electronics and Information Systems Technology, Department of Electrical & Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece

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https://doi.org/10.1142/S0218001403002502Cited by:1 (Source: Crossref)

Abstract

This paper proposes a new neuro-fuzzy technique suitable for binarization and gray-scale reduction of digital documents. The proposed approach uses both the image gray-scales and additional local spatial features. Both, gray-scales and local feature values feed a Kohonen Self-Organized Feature Map (SOFM) neural network classifier. After training, the neurons of the output competition layer of the SOFM define two bilevel classes. Using the content of these classes, fuzzy membership functions are obtained that are next used by the fuzzy C-means (FCM) algorithm in order to reduce the character-blurring problem. The method is suitable for improving blurring and badly illuminated documents and can be easily modified to accommodate any type of spatial characteristics.

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