Paper
22 February 2023 Deep learning captcha recognition for mobile based on TensorFlow
Author Affiliations +
Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 125871J (2023) https://doi.org/10.1117/12.2667721
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China
Abstract
As the most common captcha, text captcha can prevent others from maliciously using computer programs to log in or attack, and is an important safeguard in Internet authentication. In recent years, with the development of the Internet, the field of artificial intelligence has also developed at a high speed, and convolutional neural networks are widely used in various fields. In this context, for the common problem of character-based captcha recognition, this paper investigates captcha recognition based on a deep learning neural network framework used by the TensorFlow framework with modifications based on the VGG16 convolutional neural network. The 4-digit captcha randomly composed of 64 characters is then converted into an image, and after operations such as image processing and encoding of the captcha, a large number of training sets are generated and the recognition of the captcha is done by the convolutional neural network. Finally, the design GUI interface is deployed to mobile devices with a final accuracy rate of 85% on the test set.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiangfeng Lin, Linfu Li, and Yu Ren "Deep learning captcha recognition for mobile based on TensorFlow", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 125871J (22 February 2023); https://doi.org/10.1117/12.2667721
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KEYWORDS
Convolutional neural networks

Education and training

Image processing

Deep learning

Batch normalization

Data conversion

Instrument modeling

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