A Survey on Banknote Recognition Methods by Various Sensors

Abstract

Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them.

Currency Recognition and Calculation System using Machine Learning Techniques

“…AlexNet Network the training size of the images are to be resized to 227 x 227, it has 5 convolution layers and followed by the max polling layers, 2 global layers and softmax at the end [5]. In relation to the image processing, using the verification of diverse currency notes Shahbaj Khan et al [6], implies the need for converting the image from RGB to Grayscale to ease the difficulties in the preprocessing extraction for security thread. In the process, the currency edges can be identified using the Sobel operator and edge-base segmentation.…”

Deep Learning-Based Multinational Banknote Fitness Classification with a Combination of Visible-Light Reflection and Infrared-Light Transmission Images

“…Currently, automated machines for financial transactions are becoming popular and have been significantly modernized. Such facilities can handle various functionalities, including not only the recognition of banknote type, counting, sorting and detection of counterfeits, but also serial recognition and fitness classification [1]. The capability of operating on currencies from various countries and regions is also being considered.…”

Geometric and Grayscale Template Matching for Saudi Arabian Riyal Paper Currency Recognition

“…Therefore, many researchers focus on detecting the authenticity of 100, 500, and 1000 rupees based on Neural Network [5]- [10] and other image processing based methods morphologically [11]- [18]. In addition to the Indian Rupee, other currencies are widely studied, including Euro [19]- [20], US Dollar [21]- [23], Saudi Arabian Riyal [22], Indonesian Rupiah [24] and Japan Yen [25]. One study of the currency recognition based on image processing was a study that has been done by Sawant [26].…”

“…The method used for the classification was Radial Basis Function Network and the average accuracy level obtained was quite satisfactory that was 91.51% [28]. Other research this paper referred to was PCR and classification on five currencies at once, including US Dollar (USD), Australian Dollar (AUD), Saudi Arabian Riyal (SAR), Euro (EUR), and Indian Rupee (INR) [22]. In this research, pattern recognition in Region of Interest (ROI) using neural network, while the process of classification using template matching method.…”

“…Based on some of the studies presented above, PCR is still a challenging topic to study. Therefore, in this research we will study the currency recognition technique and its classification using template matching method, because based on research [22] it has successfully recognized five currencies namely USD, AUD, INR, EUR and SAR. In the previous study [28], PCR and classification of USD based on Canny Edge Detection and Template Matching obtained an average accuracy level of 95.625%.…”