Study on recognition algorithm for paper currency numbers based on neural network

Li, Xiuyan; Liu, Tiegen; Li, Yuanyao; Zhang, Zhongchuan; Deng, Sen

doi:10.1117/12.807073

Cited by 2 publications

(2 citation statements)

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“…As listed in Table 13 , algorithms for classification used in the last step of the serial number recognition process step include the Euclidean distance-based matching method [ 122 ], SVMs [ 123 ], NNs [ 124 , 128 ], and a cascaded combination of multiple classifiers [ 126 ].…”

Section: Serial Number Recognitionmentioning

confidence: 99%

A Survey on Banknote Recognition Methods by Various Sensors

Lee

Hong

Kim

et al. 2017

Sensors

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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.

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Section: Serial Number Recognitionmentioning

confidence: 99%

A Survey on Banknote Recognition Methods by Various Sensors

Lee

Hong

Kim

et al. 2017

Sensors

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“…Correct recognition of banknote serial numbers is important because it can be used to trace the circulation routes of individual banknotes to provide essential information about economic activities [1,2]. For this reason, many investigations of serial number recognition have been conducted [2,3,4,5,6,7]. The serial number recognition problem essentially belongs to the printed character recognition problem which has been studied in other fields such as license plate recognition [8] and address recognition [9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Fast Banknote Serial Number Recognition Using Knowledge Distillation and Bayesian Optimization

Choi

Chae

Kim

2019

Sensors

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We investigated a machine-learning-based fast banknote serial number recognition method. Unlike existing methods, the proposed method not only recognizes multi-digit serial numbers simultaneously but also detects the region of interest for the serial number automatically from the input image. Furthermore, the proposed method uses knowledge distillation to compress a cumbersome deep-learning model into a simple model to achieve faster computation. To automatically decide hyperparameters for knowledge distillation, we applied the Bayesian optimization method. In experiments using Japanese Yen, Korean Won, and Euro banknotes, the proposed method showed significant improvement in computation time while maintaining a performance comparable to a sequential region of interest (ROI) detection and classification method.

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Study on recognition algorithm for paper currency numbers based on neural network

Cited by 2 publications

References 0 publications

A Survey on Banknote Recognition Methods by Various Sensors

A Survey on Banknote Recognition Methods by Various Sensors

Machine Learning-Based Fast Banknote Serial Number Recognition Using Knowledge Distillation and Bayesian Optimization

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