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An Automatic Location and Recognition Method for Bank Card Number

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RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

September 2019

Pages 728 - 732

https://doi.org/10.1145/3366194.3366325

Published: 20 September 2019 Publication History

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Abstract

The Optical Character Recognition (OCR) technology is widely used in intelligent identification of bank cards, since it can improve the work efficiency and user experience in mobile payment. Conventional methods have the problems of low recognition rate and location accuracy. Therefore, an automatic location and recognition method for bank card number is proposed. Firstly, novel Connected Text Proposal Network (CTPN) algorithm is improved to locate the bank card number. Then, the Convolutional Recurrent Neural Networks (CRNN) algorithm is optimized to identify the card number. Some experimental results show that the method has a high positioning accuracy and recognition rate.

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Cheng JWu YGao WFu J(2023)A Research on Bank Card Number Recognition Based on ASFF+YOLOv7 and Multi-Scale Feature Line Fusion2023 IEEE 13th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)10.1109/CYBER59472.2023.10256479(1097-1103)Online publication date: 11-Jul-2023
https://doi.org/10.1109/CYBER59472.2023.10256479
Lin HZhan YLiu SKe XChen Y(2022)A deep learning based bank card detection and recognition method in complex scenesApplied Intelligence10.1007/s10489-021-03119-252:13(15259-15277)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10489-021-03119-2

Index Terms

An Automatic Location and Recognition Method for Bank Card Number
1. Computing methodologies
  1. Machine learning

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RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

September 2019

803 pages

ISBN:9781450372985

DOI:10.1145/3366194

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 20 September 2019

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the Jiangsu Province Natural Science Foundation
the National Natural Science Foundation of China

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RICAI 2019

RICAI 2019: 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

September 20 - 22, 2019

Shanghai, China

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RICAI '19 Paper Acceptance Rate 140 of 294 submissions, 48%;

Overall Acceptance Rate 140 of 294 submissions, 48%

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Cheng JWu YGao WFu J(2023)A Research on Bank Card Number Recognition Based on ASFF+YOLOv7 and Multi-Scale Feature Line Fusion2023 IEEE 13th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)10.1109/CYBER59472.2023.10256479(1097-1103)Online publication date: 11-Jul-2023
https://doi.org/10.1109/CYBER59472.2023.10256479
Lin HZhan YLiu SKe XChen Y(2022)A deep learning based bank card detection and recognition method in complex scenesApplied Intelligence10.1007/s10489-021-03119-252:13(15259-15277)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10489-021-03119-2

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Bank card number recognition system based on deep learning

A deep learning based bank card detection and recognition method in complex scenes

Character and numeral recognition for non-Indic and Indic scripts: a survey

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Bank card number recognition system based on deep learning

A deep learning based bank card detection and recognition method in complex scenes

Character and numeral recognition for non-Indic and Indic scripts: a survey

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