Recognition of text with known geometric and grammatical structure

The optical character recognition (OCR) module is a fundamental part of each automated text processing system. The OCR module translates an input image with a text line into a string of symbols. In many applications (e.g. license plate recognition) the text has some a priori known geometric and grammatical structure. This article proposes an OCR method exploiting this knowledge which restricts the set of possible strings to a limited set of feasible combinations. The recognition task is formulated as maximization of a similarity function which uses character templates as reference. These templates are estimated by a support vector machine method from a set of examples. In contrast to the common approach, the proposed method performs character segmentation and recognition simultaneously. The method was successfully evaluated in a car license plate recognition system.