A rule-based optical character recognition system for the recognition of serial number on Renminbi (RMB) banknote is presented. It is based on the observation that the characters, including English letters and numbers, can be classified using two hand-crafted features, which are the opening and the loop. Each character has certain characteristics in terms of those two features and classification is achieved following the proposed scheme. The proposed system has been tested on 2245 RMB bills, which contain 22313 characters, and accomplished 99.35% for horizontal characters and 99.84% for vertical characters under 30ms processing time per banknote.
This paper presents a new vision based approach to vehicle re-identification (VRI) for smart transportation systems by fusion of multiple features. Unlike the conventional VRI systems which adopted loop sensors to capture inductive features for classification, we developed a hierarchical method for VRI by coarse-to-fine image matching. More specifically, VRI is performed at fine level by image matching using distinctive and anonymous features which are extracted from the large number of interesting points detected from the vehicle and its license plate images at coarse level. To achieve robustness, the thresholding of matching criteria is based on the dynamic analysis of the time series of vehicle images rather than predefined. In addition, the fusion of multiple features is conducted via a weighted probability scheme. To demonstrate the feasibility of the proposed new approach, a series of field testing were conducted, where 301 vehicles were considered for data calibration and 1699 vehicles were used for validation tests. The accuracy of matching rate reaches 73.51%. 85.52% and greater than 90% respectively by using density features, fusion of selected distinctive features and fusion of multimodal features.