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Graph cut-based binarisation of noisy check images

Graph cut-based binarisation of noisy check images

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Binarisation of document images with poor contrast, strong noise, complex patterns and variable modalities in the grey-scale histograms is a challenging problem. This study proposes an algorithm for the binarisation of noisy check images to extract handwriting text using normalised graph cuts (GCs). The proposed algorithm uses a normalised GC measure as a thresholding principle to distinguish the handwriting characters from the noisy background. The authors propose a factor to penalise extracting objects that do not have the elongated shape of the characters. Improving the structural quality of the characters' skeleton facilitates better feature extraction and classification, which improves the overall performance of optical character recognition (OCR). Experimental results performed on 560 check images showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms.

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