Abstract
In this chapter, a binarization technique specifically designed for historical document images is presented. Existing binarization techniques focus either on finding an appropriate global threshold or adapting a local threshold for each area in order to remove smear, strains, uneven illumination etc. Here, a hybrid approach is presented that first applies a global thresholding technique and, then, identifies the image areas that are more likely to still contain noise. Each of these areas is re-processed separately to achieve better quality of binarization. Evaluation results are presented that compare our technique with existing ones and indicate that the proposed approach is effective, combining the advantages of global and local thresholding. Finally, future directions of our research are mentioned.
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Sokratis, V., Kavallieratou, E., Paredes, R., Sotiropoulos, K. (2011). A Hybrid Binarization Technique for Document Images. In: Biba, M., Xhafa, F. (eds) Learning Structure and Schemas from Documents. Studies in Computational Intelligence, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22913-8_8
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DOI: https://doi.org/10.1007/978-3-642-22913-8_8
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