TY - JOUR
T1 - A survey on banknote recognition methods by various sensors
AU - Lee, Ji Woo
AU - Hong, Hyung Gil
AU - Kim, Ki Wan
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2017 by the authors; licensee MDPI, Basel, Switzerland.
PY - 2017/2/8
Y1 - 2017/2/8
N2 - 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.
AB - 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.
KW - Banknote recognition
KW - Counterfeit banknote detection
KW - Fitness classification
KW - Serial number recognition
KW - Various sensors
UR - http://www.scopus.com/inward/record.url?scp=85012293099&partnerID=8YFLogxK
U2 - 10.3390/s17020313
DO - 10.3390/s17020313
M3 - Review article
C2 - 28208733
AN - SCOPUS:85012293099
SN - 1424-3210
VL - 17
JO - Sensors
JF - Sensors
IS - 2
M1 - 313
ER -