A multi-scale framework for adaptive binarization of degraded document images
Pattern Recognition
A new binarization method for non-uniform illuminated document images
Pattern Recognition
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Segmentation of ancient documents is challenging. In the worst cases, text characters become fragmented as the results of strong degradation processes. New active contour methods allow to handle difficult cases in a spatially coherent fashion. However, most of those method use a restrictive, a priori shape information that limit their application. In this work, we propose to address this issue by combining two complementary approaches. First, multi-level classifiers, which take advantage of the stroke width a priori information, allow to locate candidate character pixels. Second, a level set active contour scheme is used to identify the boundary of a character. Tests have been conducted on a set of ancient degraded Hebraic character images. Numerical results are promising.