Abstract

In general, dirty banknotes that have creases or soiled surfaces should be replaced by new banknotes, whereas clean banknotes should be recirculated. Therefore, the accurate classification of banknote fitness when sorting paper currency is an important and challenging task. Most previous research has focused on sensors that used visible, infrared, and ultraviolet light. Furthermore, there was little previous research on the fitness classification for Indian paper currency. Therefore, we propose a new method for classifying the fitness of Indian banknotes, with a one-dimensional line image sensor that uses only visible light. The fitness of banknotes is usually determined by various factors such as soiling, creases, and tears, etc. although we just consider banknote soiling in our research. This research is novel in the following four ways: first, there has been little research conducted on fitness classification for the Indian Rupee using visible-light images. Second, the classification is conducted based on the features extracted from the regions of interest (ROIs), which contain little texture. Third, 1-level discrete wavelet transformation (DWT) is used to extract the features for discriminating between fit and unfit banknotes. Fourth, the optimal DWT features that represent the fitness and unfitness of banknotes are selected based on linear regression analysis with ground-truth data measured by densitometer. In addition, the selected features are used as the inputs to a support vector machine (SVM) for the final classification of banknote fitness. Experimental results showed that our method outperforms other methods.

Highlights

  • In recent years, automatic payment facilities such as vending machines and automatic teller machines (ATMs) have become more and more popular

  • We proposed a method based on a discrete wavelet transform (DWT) of grayscale Rupee banknote images captured only by a visible light sensor

  • We find that the average EER (0.3655%) by Daubechies discrete wavelet transformation (DWT) is slightly lower than the average EER (0.4693%) by Haar DWT

Read more

Summary

Introduction

Automatic payment facilities such as vending machines and automatic teller machines (ATMs) have become more and more popular. The importance of correctly recognizing and classifying banknotes has increased. This problem consists of automatically sorting banknotes by denominations, sides, and directions, and in determining the fitness of those banknotes. We mean determining which banknotes are suitable for recirculation and which should be replaced by new ones. If a fit banknote is recirculated frequently, the cost for printing that banknote can be greatly reduced [1]. If an unfit banknote is replaced with a new one, the processing speed and accuracy of banknote dispensing in ATMs can be greatly enhanced

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call