Chinese Handwritten Legal Amount Recognition with HMM-Based Approach

A hidden Markov model (HMM) based method for Chinese legal amount recognition is presented in this paper. In the training phase, gradient feature is extracted from sliding windows and character HMMs are trained with single character images. In the recognition phase, the text line image is segmented using sentence HMM, which is constructed by character HMMs according to a strict language model. The main difference between our proposed method and traditional methods is that our segmentation is guided by language model, which can solve many tough segmentation problems. Moreover, we combine the HMM-based method with traditional OCR method to improve the recognition accuracy. Experiments have been performed on 4,709 legal amount text line images extracted from real-life bank checks. The recognition rate is 97.13%.