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Lim B, Son S, Kim H, et al (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Long Beach, USA, pp 510–519 Li X, Wang W, Hu X, et al (2019) Selective kernel networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Hawaii, USA, pp 4700–4708 Huang G, Liu Z, Laurens V, et al (2017) Densely connected convolutional networks. Huang S, Li F, Chen Q (2020) Computational tomography image classification algorithm based on improved deep residual network. Huang S, Chen Q (2020) Computed tomography image classification algorithm based on improved deep residual network.
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In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition. He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. Han YF (2017) The current situation and analysis of the digitization of Chinese classical literature. Guo LM, Ge L, Liu YR (2019) The practice of convolutional neural network in ancient Chinese character recognition. General Office of the State Council on further strengthening the protection of ancient books (2007) Bulletin of the State Council of the People's Republic of China 06(08):12–14 The experimental results show that the accuracy and precision of recognition using this paper's network can reach 93.48% and 95.37%, which is a significant improvement compared with other recognition methods, and proves that SKRDN has good advantages in dealing with the recognition problem of ancient Chinese characters.īaidu. The network fully considers the high sensitivity of residual dense network to data fluctuation and uses selective convolution kernel to construct residual dense blocks to provide a dynamic selection mechanism for the receptive fields of different images, so that it can focus on the fine structure of Chinese characters and thus realize cross-channel information interaction and integration. In order to improve the utilization of its content, and to improve the difficult recognition problem caused by the complex character glyphs and diverse styles of Chinese characters in ancient books, as well as the single and double column mismatch and the different sizes of Chinese characters, in this paper, we propose an adaptive receptive field-based image recognition network for Chinese ancient characters. However, the phenomenon of “Heavy storage and light use” is serious in modern society. As the main carrier of Chinese civilization, the importance of ancient Chinese documents cannot be overstated.