Authors:
Jan Rathouský
1
;
Martin Urban
2
and
Vojtěch Franc
3
Affiliations:
1
Faculty of Elec. Eng., Czech Technical University in Prague, Czech Republic
;
2
Eyedea Recognition; Center for Applied Cybernetics, Faculty of Elec. Eng., Czech Technical University in Prague, Czech Republic
;
3
Fraunhofer Institut FIRST IDA, Germany
Keyword(s):
Text Recognition, Sructured Support Vector Machines, License Plate Recognition.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
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.