A feasibility study of deep neural networks for the recognition of banknotes regarding central bank requirements

• Main Authors: Schulte, Julia, Staps, Daniel, Lampe, Alexander
• Format: Journal Article
• Language: English
• Published: 18-07-2019
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Statistics - Machine Learning
• DOI: 10.48550/arxiv.1907.07890

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.