There are many challenges addressed in handwritten document image binarization, such as faint characters, remove white borders from full bleed word pdf-through and large background ink stains. Usually, binarization methods cannot deal with all the degradation types effectively.
Motivated by the low detection rate of faint characters in binarization of handwritten document images, a combination of a global and a local adaptive binarization method at connected component level is proposed that aims in an improved overall performance. Initially, background estimation is applied along with image normalization based on background compensation. Afterwards, global binarization is performed on the normalized image. In the binarized image very small components are discarded and representative characteristics of a document image such as the stroke width and the contrast are computed.
Furthermore, local adaptive binarization is performed on the normalized image taking into account the aforementioned characteristics. Finally, the two binarization outputs are combined at connected component level. A combined approach for binarization with high overall performance. Modified global logarithmic contrast computation. Combination at connected component level. Check if you have access through your login credentials or your institution.
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