Multi-resolution character recognition by adaptive classification
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Display text segmentation after learning best-fitted OCR binarization parameters
Expert Systems with Applications: An International Journal
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The character image database plays an important role for the evaluation of a character recognition system. But there is no measure which tells the level of recognition difficulty of a given database. This paper proposes a novel approach for the low resolution character recognition, which fits the input character for the appropriate character database according to the input image quality. It is composed of two stems: character image quality evaluation, character recognition. Firstly, it presents the gray distribution feature to evaluate the character image quality. Secondly, according to the evaluation result the appropriate character database and recognition method are selected for the input character image which makes the classification have the higher probability of being the correct decision. Experiment results demonstrate the proposed approach highly improved the performance of the degraded character recognition system.