RECOGNITION OF PRINTED TEXT UNDER REALISTIC CONDITIONS

Past research in OCR has focused on the shape analysis of binarized images, quite often assuming good quality document and isolated characters. Such assumptions are challenged by the conditions met in practice: binarization is difficult for low contrast documents, characters often touch each other, not only on the sides but also between lines, etc. After a brief review of past work we will describe current efforts to deal with OCR as a signal processing problem where the causes of noise and distortions as well the idealized images (definitions of typefaces) are modeled and subjected to a quantitative analysis. The key idea of the analysis is that while printed text images may be binary in an ideal state. the images seen by the sensors are gray scale because of convolution distortion and other causes. Therefore binarization should be carried out at the same time as feature extraction.