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Given a noisy text page, a word recognizer can generate a set of candidates for each word image. A relaxation algorithm was proposed previously by the authors that uses word collocation statistics to select the candidate for each word that has the highest probability of being the correct decision. Because word collocation is a local constraint and collocation data trained from corpora are usually incomplete, the algorithm cannot select the correct candidates for some images. To overcome this limitation, contextual information at the image level is now exploited inside the relaxation algorithm. If two word images can match with each other, they should have same symbolic identity. Visual inter-word relations provide a way to link word images in the text and to interpret them systematically. By integrating visual inter-word constraints with word collocation data, the performance of the relaxation algorithm is improved.

Degraded text recognition using word collocation and visual inter-word constraints