Morphological filter for text extraction from textured background

A new method for text extraction from binary images with a textured background is proposed. Text extraction in such a case is very important for successful character recognition. because many character recognition methods expect text printed on a uniform (and typically white) background and their performance significantly degrades if this condition is not satisfied. The methods that have been already proposed to solve this problem, attempt to extract primitives or elements composing the textured background in order to separate text from them. From experiments with commercial character recognition software we observed that such an approach easily leads to the significant growth of errors in character recognition because of degradations in extracted characters, introduced during text extraction. On the other hand. it is hardly possible to reconstruct (more or less precisely) the degraded characters without knowning their class labels and this information is not Yet available at this stage. In contrast. we explore another approach similar to symbolic compression of text, which is implemented as a morphological filter using the top-hat transform. This approach detects characters having similar shapes from an original image and it thus avoids character degradations. As a result. the accuracy of character recognition can be improved.