Cut classification for segmentation

In optical character recognition (OCR) and document analysis, many reading errors are not caused by inadequate classifier power, but by segmentation errors. In particular, merged characters are a major remaining problem. An efficient and powerful method of determining cut hypotheses for the segmentation of merged characters is presented. The method is based on a classifier deciding for each column of the character image, whether it represents a cut hypothesis or not. Since in the training phase the classifier is adapted by a sample set consisting of images of merged character patterns, the decision rules are created automatically rather than being man-made heuristics. The results obtained from a large test set show that a high recognition rate can be achieved with a reasonable computational effort