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In this paper, we present a prototype system of courtesy amount recognition (CAR) for Chinese bank checks. The system deals with color check images and consists of five modules: skew correction, binarization, string extraction, segmentation and recognition. The whole system is designed under three principles: information fusion, complementary method combination and multi-hypotheses generation then evaluation. Information from color, gray and binary images is fused and complementary algorithms are applied for binarization and string extraction. Multi-hypotheses are made by keeping all possible candidates when ambiguous solutions exist in extraction and segmentation. Then the most suitable one is selected as the final result by evaluating the probabilities from recognition. Experiments show that our system is very promising based on a large number of real checks collected from different banks.