Part-Based High Accuracy Recognition of Serial Numbers in Bank Notes

Abstract

This paper proposes a novel part-based character recognition method for a new topic of RMB (renminbi bank note, the paper currency used in China) serial number recognition, which is important for reducing financial crime and improving financial market stability and social security. Given an input sample, we first generate a set of local image parts using the Difference-of-Gaussians (DoG) keypoint detector. Then, all of the local parts are classified by an SVM classifier to provide a confidence vector for each part. Finally, three methods are introduced to combine the recognition results of all parts. Since the serial number samples suffer from complex background, occlusion, and degradation, our part-based method takes advantage of both global and local character structure features, and offers an overall increase in robustness and reliability to the entire recognition system. Experiments conducted on a RMB serial number character database show that the test accuracy boosted from 98.90% to 99.33% by utilizing the proposed method with multiple voting based combination strategy. The part-based recognition method can also be extended to other types of banknotes, such as Euro, U.S. and Canadian dollars, or in character recognition applications with complex backgrounds.