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Xiangfeng Lin

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Proceedings Article | 22 February 2023 Paper

Deep learning captcha recognition for mobile based on TensorFlow

Xiangfeng Lin, Linfu Li, Yu Ren

Proceedings Volume 12587, 125871J (2023) https://doi.org/10.1117/12.2667721

KEYWORDS: Education and training, Convolutional neural networks, Image processing, Deep learning, Machine learning, Instrument modeling, Data conversion, Batch normalization, Matrices, Convolution

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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.

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