Serial Number Extracting and Recognizing Applied in Paper Currency Sorting System Based on RBF Network

Liu, Li; Ye, Yutang; Xie, Ying; Liao, Pu

doi:10.1109/cise.2010.5677049

Cited by 9 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As presented in Table 6 , there are a variety of methods for identifying anti-counterfeiting features by extracting features related to brightness information [ 83 , 84 ], fluorescence characteristics [ 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ], fidelity of the serial number and printing [ 93 , 94 ], and security threads [ 95 ]. Simple methods using only wavelengths of the visible light spectrum have limitations in accurately identifying anti-counterfeiting features.…”

Section: Counterfeit Banknote Detectionmentioning

confidence: 99%

A Survey on Banknote Recognition Methods by Various Sensors

Lee

Hong

Kim

et al. 2017

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Counterfeit Banknote Detectionmentioning

confidence: 99%

A Survey on Banknote Recognition Methods by Various Sensors

Lee

Hong

Kim

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…Images are converted to grayscale [17,18,19], new values are generated for each pixel, compressing the amount of data to be analyzed reducing complexity. New pixel values are generated from grayscale values using a linear transform function.…”

Section: A Featuresmentioning

confidence: 99%

“…Traditional line fitting is insufficient for banknote recognition, as images contain false edge points [17]. Hough transform is used to detect edges of the paper, removing distortion borrowing from Optical Character Recognition (OCR) techniques.…”

Section: ) Authenticating Security Componentsmentioning

confidence: 99%

Currency security and forensics: a survey

Chambers

Yan

Garhwal

et al. 2014

Multimed Tools Appl

View full text Add to dashboard Cite

Abstract-By its definition, the word 'currency' refers to an agreed medium for exchange, a nation's currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desired.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Serial Number Extracting and Recognizing Applied in Paper Currency Sorting System Based on RBF Network

Cited by 9 publications

References 4 publications

A Survey on Banknote Recognition Methods by Various Sensors

A Survey on Banknote Recognition Methods by Various Sensors

Currency security and forensics: a survey

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

Contact Info

Product

Resources

About