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A neuro-fuzzy technique for document binarisation

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Abstract

This paper proposes a new neuro-fuzzy technique suitable for binarisation or, in general, the colour reduction of digital documents. The proposed approach uses the image colour values and additional local spatial features extracted in the neighbourhood of the pixels. Both image and local features values feed a Kohonen self-organised feature map (SOFM) neural network classifier. After training, the neurons of the output competition layer of the SOFM define a first approach of the final 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 obtain the colours of the final document. The method can be applied to greyscale and colour documents; it 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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Authors and Affiliations

  1. Department of Electrical & Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece

    Nikos Papamarkos

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  1. Nikos Papamarkos
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Correspondence to Nikos Papamarkos.

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Papamarkos, N. A neuro-fuzzy technique for document binarisation. Neural Comput & Applic 12, 190–199 (2003). https://doi.org/10.1007/s00521-003-0382-z

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  • DOI: https://doi.org/10.1007/s00521-003-0382-z

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