Complex character decomposition using deformable model

Despite the fact that Chinese characters are composed of radicals and Chinese people usually formulate their knowledge of Chinese characters as a combination of radicals, very few studies have focused on a character decomposition approach to recognition, i.e. recognizing a character by first extracting and recognizing its radicals. In this paper, such an approach is adopted, and the problem of how to extract radical sub-images from character images is addressed by proposing an algorithm based on a deformable model (DM). The application of a DM to complex character decomposition (and recognition) is a novel one, and concepts like goodness of character decomposition have been exploited to formulate appropriate energy terms and to devise cost-effective minimization schemes for the problem. The advantage of the character decomposition approach is demonstrated by feeding the extracted radical images to an existing structure-based Chinese character recognizer, the outputs of which are then combined to classify the input. Simulation results show that the performance of the existing system can be improved significantly when character decomposition is used.