Gabor Filters for Degraded Document Image Binarization
Most of the classical methods for degraded document binarization are based on the pixel gray level intensity or on simple pixel neighborhood information such as mean or variance to compute the binarization threshold. Moreover, these information are extracted from the spatial domain of the document image which are not very discriminative. In this paper, we propose to estimate texture information based on Gabor filters for ancient degraded documents. First, the dominant slant angle of the document image script is computed by using the Fourier transform. Then, this dominant angle is used within a weighted sum of angles in a Gabor filter bank in order to capture more efficiently the document image foreground (text). This information, combined with the variance and the mean extracted respectively from spatial and frequency domains are used for estimating the binarization threshold. Three variants are used for evaluating the performance of Gabor filter bank, which are based on Niblack's, Sauvola's, and Wolf's thresholds. Experimental results conducted on DIBCO Datasets show that the proposed method is more appropriate for poor contrasted documents and ink-bleed through degradations.