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Low-Quality Banknote Serial Number Recognition Based on Deep Neural Network

  • Jang, Unsoo (Dept. of Computer Science, Sangmyung University) ;
  • Suh, Kun Ha (Dept. of Computer Science, Sangmyung University) ;
  • Lee, Eui Chul (Dept. of Human Centered Artificial Intelligence, Sangmyung University)
  • Received : 2018.01.16
  • Accepted : 2018.11.27
  • Published : 2020.02.29

Abstract

Recognition of banknote serial number is one of the important functions for intelligent banknote counter implementation and can be used for various purposes. However, the previous character recognition method is limited to use due to the font type of the banknote serial number, the variation problem by the solid status, and the recognition speed issue. In this paper, we propose an aspect ratio based character region segmentation and a convolutional neural network (CNN) based banknote serial number recognition method. In order to detect the character region, the character area is determined based on the aspect ratio of each character in the serial number candidate area after the banknote area detection and de-skewing process is performed. Then, we designed and compared four types of CNN models and determined the best model for serial number recognition. Experimental results showed that the recognition accuracy of each character was 99.85%. In addition, it was confirmed that the recognition performance is improved as a result of performing data augmentation. The banknote used in the experiment is Indian rupee, which is badly soiled and the font of characters is unusual, therefore it can be regarded to have good performance. Recognition speed was also enough to run in real time on a device that counts 800 banknotes per minute.

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