Application of support vector machine (SVM) on serial number identification of RMB

Serial numbers identification of RMB (the name of Chinese paper currency) is a nonlinear and high dimensions pattern recognition problem which sample is limited. It is one of many difficulty problems in pattern recognition. It also has great research and practical value. This thesis studies the multi-class optimize algorithm in statistical learning theory, analyzes SMOD algorithm and its precondition of serial number recognition. It applies the support vector machine into the serial number´s machine recognition of paper currency. It puts forward the theory of serial number identification which based on SVM method, establishes the identification process of identification by SVM. Then we write the number identification algorithm and carrying on simulation test. The experimental results proved that sequential minimal optimized SVM has fairly low computing load and high precision of recognition. It fully shows the advantages of SVM in solving limited samples, non-linear and high dimension pattern recognition problems. Compared to neural network and fuzzy theory algorithm, its computing load is fairly low. So it can be easily realized with embedded controller.