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Adaptive binarization method for degraded document images based on surface contrast variation

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Abstract

Document binarization is an important technique in document image analysis and recognition. Generally, binarization methods are ineffective for degraded images. Several binarization methods have been proposed; however, none of them are effective for historical and degraded document images. In this paper, a new binarization method is proposed for degraded document images. The proposed method based on the variance between pixel contrast, it consists of four stages: pre-processing, geometrical feature extraction, feature selection, and post-processing. The proposed method was evaluated based on several visual and statistical experiments. The experiments were conducted using five International Document Image Binarization Contest benchmark datasets specialized for binarization testing. The results compared with five adaptive binarization methods: Niblack, Sauvola thresholding, Sauvola compound algorithm, NICK, and Bataineh. The results show that the proposed method performs better than other methods in all binarization cases.

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Authors and Affiliations

  1. Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Bilal Bataineh, Siti Norul Huda Sheikh Abdullah & Khairuddin Omar

Authors
  1. Bilal Bataineh
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  2. Siti Norul Huda Sheikh Abdullah
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  3. Khairuddin Omar
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Correspondence to Bilal Bataineh.

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Bataineh, B., Abdullah, S.N.H.S. & Omar, K. Adaptive binarization method for degraded document images based on surface contrast variation. Pattern Anal Applic 20, 639–652 (2017). https://doi.org/10.1007/s10044-015-0520-0

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