A technique for fuzzy document binarization

ABSTRACT

This paper proposes a new method for fuzzy binarization of digital document. The proposed approach achieves binarization using both the image gray-levels and additional local spatial features. Both, gray-level and local features 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 content of these classes, fuzzy membership functions are obtained that are next used with the Fuzzy C-means (FCM) algorithm in order to reduce the character-blurring problem. The method is suitable for binarization of blurring documents and can be easily modified to accommodate any type of spatial characteristics.

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Index Terms

• Applied computing

  • Document management and text processing

    • Document capture

• Computing methodologies

  • Artificial intelligence

    • Knowledge representation and reasoning

      • Probabilistic reasoning

      • Vagueness and fuzzy logic