Segmentation-driven offline handwritten Chinese and Arabic script recognition
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Arabic bank check analysis and zone extraction
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
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This paper presents a novel research investigation on legal amount recognition of unconstrained cursive handwritten Chinese character in the environment of A2iA CheckReader驴 - a commercial bank check recognition system. The following problems and their solutions are described: character set of Chinese legal amounts, preprocessing (slant detection and correction), segmentation, feature extraction, grammar, automatic annotation of Chinese characters before and during training, and neural network/hidden Markov model training and recognition. The system is trained with 47.8 thousand real bank checks, and validated with 12 thousand real bank checks. The recognition rate at the character level is 93.5%, and the recognition rate at the legal amount level is 60%. This is the first successful commercial product in this domain.