Monetary Recognition Based On Support Vector Machines

SVM algorithms and currency recognition based on support vector machines are mainly discussed in this paper. Currency recognition which is a scared samples, nonlinear and high dimensions pattern recognition problem is one of the difficult problems of modern pattern recognition and of specific research significance and practical value. The Support Vector Machines is a new machines learning algorithm based on the Statistical Learning Theory. It has been applied in pattern recognition of scared samples, nonlinear and high dimensions character vectors.A new fast algorithm of support vector machines and the concept of "hullvector" are proposed. Using the geometric information of the training samples, the algorithm first extracts the set of hullvectors, which are most likely to be the support vector. Then the set of hullvectors are trained as the new training samples to get the support vectors. HVSVM reduces the time consumed by the QP problem in the SVM training in large degree, and highly speeds the whole training process of SVM.A hierarchical Support Vector Machines classify tree based on kernel clustering method combining the unsupervised learning method and supervised learning together is proposed to apply. It proves the algorithm is more effective and simple in structure and performance better than the original algorithm.The algorithm of Support Vector Machines has been studied in this paper, according to the specialty of Support Vector Machines, corresponding method of obtaining and advance processing currency character data recognition is applied to currency recognition, and the HVSVM training algorithm and hierarchical SVM tree of multi-values classify method is proposed to used. Consequently, efficient and accurate paper currency recognition is achieved, and the results show the validity of the project.