{'fraction_non_alphanumeric': 0.02063273727647868, 'fraction_numerical': 0.0, 'mean_word_length': 5.442477876106195, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Paper Currency Verification with Support Vector Machines

Distinct from conventional techniques where the neural network (NN) is employed to solve the problem of paper currency verification, in this paper, we shall present a novel method by applying the support vector machine (SVM) approach to distinguish counterfeit banknotes from genuine ones. On the basis of the statistical learning theory, SVM has better generalization ability and higher performance especially when it comes to pattern classification. Besides, discrete wavelet transformation (DWT) will also be applied so as to reduce the input scale of SVM. Finally, the results of our experiment will show that the proposed method does achieve very good performance.