Context driven text segmentation and recognition

We present an algorithm for text segmentation and recognition mainly suited for complex problems where many merged characters are present. The basic idea is to define a distance, between lines of text and strings, which allows us to postpone the final decision about text segmentation and character classification until the contextual analysis is performed. The distance takes into account both the hypotheses about segmentation generated by a text segmentation module and the hypotheses about character classification produced by a probabilistic classifier. The algorithm has been tested by reading text on books’ covers; the experimental results highlight the quality of the solution proposed.