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CAPTCHA Recognition Using Deep Learning with Attached Binary Images

1
College of Internet of Things (IoT) Engineering, Hohai University, Changzhou Campus, Changzhou 213022, China
2
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
3
College of Communication Engineering, Jilin University, Changchun 130061, China
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Electronic Engineering and Information Science Department, University of Science and Technology of China, Hefei 230026, China
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(9), 1522; https://doi.org/10.3390/electronics9091522
Received: 4 August 2020 / Revised: 3 September 2020 / Accepted: 9 September 2020 / Published: 17 September 2020
(This article belongs to the Section Artificial Intelligence)
Websites can increase their security and prevent harmful Internet attacks by providing CAPTCHA verification for determining whether end-user is a human or a robot. Text-based CAPTCHA is the most common and designed to be easily recognized by humans and difficult to identify by machines or robots. However, with the dramatic advancements in deep learning, it becomes much easier to build convolutional neural network (CNN) models that can efficiently recognize text-based CAPTCHAs. In this study, we introduce an efficient CNN model that uses attached binary images to recognize CAPTCHAs. By making a specific number of copies of the input CAPTCHA image equal to the number of characters in that input CAPTCHA image and attaching distinct binary images to each copy, we build a new CNN model that can recognize CAPTCHAs effectively. The model has a simple structure and small storage size and does not require the segmentation of CAPTCHAs into individual characters. After training and testing the proposed CAPTCHA recognition CNN model, the achieved experimental results reveal the strength of the model in CAPTCHA character recognition. View Full-Text
Keywords: recognition; text-based CAPTCHA; convolutional neural network; deep learning; digital image processing recognition; text-based CAPTCHA; convolutional neural network; deep learning; digital image processing
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MDPI and ACS Style

Thobhani, A.; Gao, M.; Hawbani, A.; Ali, S.T.M.; Abdussalam, A. CAPTCHA Recognition Using Deep Learning with Attached Binary Images. Electronics 2020, 9, 1522. https://doi.org/10.3390/electronics9091522

AMA Style

Thobhani A, Gao M, Hawbani A, Ali STM, Abdussalam A. CAPTCHA Recognition Using Deep Learning with Attached Binary Images. Electronics. 2020; 9(9):1522. https://doi.org/10.3390/electronics9091522

Chicago/Turabian Style

Thobhani, Alaa, Mingsheng Gao, Ammar Hawbani, Safwan T.M. Ali, and Amr Abdussalam. 2020. "CAPTCHA Recognition Using Deep Learning with Attached Binary Images" Electronics 9, no. 9: 1522. https://doi.org/10.3390/electronics9091522

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