Evaluation of Ancient Documents and Images by using Phase Based Binarization

Segmentation of text from badly degraded document images are a very challenging task due to the high inters/intravariation between the document background and the foreground text of different document images. These features are the maximum moment of phase congruency covariance, a locally weighted mean phase angle, and a phase preserved denoised image. The proposed model consists of three standard steps: 1) preprocessing; 2) main binarization; and 3) postprocessing. In the preprocessing and main binarization steps, the features used are mainly phase derived, while in the postprocessing step, specialized adaptive Gaussian and median filters are considered. One of the outputs of the binarization step, which shows high recall performance, is used in a proposed postprocessing method to improve the performance of other binarization methodologies. Finally, we develop a ground truth generation tool, called PhaseGT, to simplify and speed up the ground truth generation process for ancient document images. The comprehensive experimental results on the DIBCO’09, HDIBCO’10, DIBCO’11, H-DIBCO’12, DIBCO’13, PHIBD’12, and BICKLEY DIARY data sets show the robustness of the proposed binarization method on various types of degradation and document images. Experiments on the Bickley diary dataset that consists of several challenging bad quality document images also show the superior performance of our proposed method, compared with other