Automatic recognition of serial numbers in bank notes

• We study a new topic of automatic recognition of bank note serial numbers.
• A new RMB serial numbers database is released.
• The paper presents an evaluation of state-of-the-art methods on RMB database.
• The enhancements of three types of distortion methods are compared.
• A novel cascade rejection scheme is proposed to achieve high reliability.

This paper presents a new topic of automatic recognition of bank note serial numbers, which will not only facilitate the prevention of forgery crimes, but also have a positive impact on the economy. Among all the different currencies, we focus on the study of RMB (renminbi bank note, the paper currency used in China) serial numbers. For evaluation, a new database NUST-RMB2013 has been collected from scanned RMB images, which contains the serial numbers of 35 categories with 17,262 training samples and 7000 testing samples in total. We comprehensively implement and compare two classic and one newly merged feature extraction methods (namely gradient direction feature, Gabor feature, and CNN trainable feature), four different types of well-known classifiers (SVM, LDF, MQDF, and CNN), and five multiple classifier combination strategies (including a specially designed novel cascade method). To further improve the recognition accuracy, the enhancements of three different kinds of distortions have been tested. Since high reliability is more important than accuracy in financial applications, we introduce three rejection schemes of first rank measurement (FRM), first two ranks measurement (FTRM) and linear discriminant analysis based measurement (LDAM). All the classifiers and classifier combination schemes are combined with different rejection criteria. A novel cascade rejection measurement achieves 100% reliability with less rejection rate compared with the existing methods. Experimental results show that MQDF reaches the accuracy of 99.59% using the gradient direction feature trained with gray level normalized data; the cascade classifier combination achieves the best performance of 99.67%. The distortions have been proved to be very helpful because the performances of CNNs boost at least 0.5% by training with transformed samples. With the cascade rejection method, 100% reliability has been obtained by rejecting 1.01% test samples.