A feasibility study of deep neural networks for the recognition of banknotes regarding central bank requirements
It addresses the specific problem of banknote recognition for the ATM and high-speed sorting industry, but is incremental as it applies existing methods to a specialized domain.
This paper investigated the feasibility of using deep neural networks to classify Euro banknotes under central bank requirements, focusing on few-class scenarios and rejection of non-class images, achieving results that meet industry standards for ATMs and sorting machines.
This paper contains a feasibility study of deep neural networks for the classification of Euro banknotes with respect to requirements of central banks on the ATM and high speed sorting industry. Instead of concentrating on the accuracy for a large number of classes as in the famous ImageNet Challenge we focus thus on conditions with few classes and the requirement of rejection of images belonging clearly to neither of the trained classes (i.e. classification in a so-called 0-class). These special requirements are part of frameworks defined by central banks as the European Central Bank and are met by current ATMs and high speed sorting machines. We also consider training and classification time on state of the art GPU hardware. The study concentrates on the banknote recognition whereas banknote class dependent authenticity and fitness checks are a topic of its own which is not considered in this work.