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[2]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
[3]. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative Adversarial Networks. arXiv preprint arXiv:1406.2661.
[4]. Jiang, W., & Zhang, L. (2018). Geospatial data to images: A deep-learning framework for traffic forecasting. Tsinghua Science and Technology, 24(1), 52-64.
[5]. Zheng, Y., & Jiang, W. (2022). Evaluation of Vision Transformers for Traffic Sign Classification. Wireless Communications and Mobile Computing, 2022.
[6]. Jiang, W. (2021). Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 184, 115537.
[7]. Zhou, H., Xiong, H., Li, C., Jiang, W., Lu, K., Chen, N., & Liu, Y. (2021). Single image dehazing based on weighted variational regularized model. IEICE TRANSACTIONS on Information and Systems, 104(7), 961-969.
[8]. Zhou, H., Zhang, Z., Liu, Y., Xuan, M., Jiang, W., & Xiong, H. (2021). Single Image Dehazing Algorithm Based on Modified Dark Channel Prior. IEICE TRANSACTIONS on Information and Systems, 104(10), 1758-1761.
[9]. Granell, E., Chammas, E., Likforman-Sulem, L., Martínez-Hinarejos, C. D., Mokbel, C., & Cîrstea, B. I. (2018). Transcription of spanish historical handwritten documents with deep neural networks. Journal of Imaging, 4(1), 15.
[10]. Wang, M., & Deng, W. (2022). Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms. arXiv preprint arXiv:2205.09442.
[11]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
[12]. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
[13]. Breiman, L. (1996). Bagging predictors. Machine learning, 24, 123-140.
[14]. Zhou, Z. H. (2012). Ensemble methods: foundations and algorithms. CRC press.
[15]. Jiang, W. (2020). MNIST-MIX: a multi-language handwritten digit recognition dataset. IOP SciNotes, 1(2), 025002.
[16]. Jiang, W. (2020, May). Evaluation of deep learning models for Urdu handwritten characters recognition. In Journal of Physics: Conference Series (Vol. 1544, No. 1, p. 012016). IOP Publishing.
[17]. Jiang, W., & Zhang, L. (2020). Edge-siamnet and edge-triplenet: New deep learning models for handwritten numeral recognition. IEICE Transactions on Information and Systems, 103(3), 720-723.
[18]. Kibriya, H., Rafique, R., Ahmad, W., & Adnan, S. M. (2021, January). Tomato leaf disease detection using convolution neural network. In 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) (pp. 346-351). IEEE.
[19]. Flad, R. K. (2008). Divination and power: a multiregional view of the development of oracle bone divination in early China. Current Anthropology, 49(3), 403-437.
[20]. An, S., Lee, M., Park, S., Yang, H., & So, J. (2020). An ensemble of simple convolutional neural network models for MNIST digit recognition. arXiv preprint arXiv:2008.10400.
[21]. Jiang, W., & Luo, J. (2022). Graph neural network for traffic forecasting: A survey. Expert Systems with Applications, 117921.
[22]. Jiang, W. (2022). Graph-based deep learning for communication networks: A survey. Computer Communications, 185, 40-54.
Cite this article
Wang,Z. (2023). Ancient character recognition with deep learning techniques. Applied and Computational Engineering,9,194-203.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Chen, S. S., & Zhang, M. (1988, March). Adaptive (neural network) control in computer-integrated-manufacturing. In Applications of Artificial Intelligence VI (Vol. 937, pp. 470-473). SPIE.
[2]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
[3]. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative Adversarial Networks. arXiv preprint arXiv:1406.2661.
[4]. Jiang, W., & Zhang, L. (2018). Geospatial data to images: A deep-learning framework for traffic forecasting. Tsinghua Science and Technology, 24(1), 52-64.
[5]. Zheng, Y., & Jiang, W. (2022). Evaluation of Vision Transformers for Traffic Sign Classification. Wireless Communications and Mobile Computing, 2022.
[6]. Jiang, W. (2021). Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 184, 115537.
[7]. Zhou, H., Xiong, H., Li, C., Jiang, W., Lu, K., Chen, N., & Liu, Y. (2021). Single image dehazing based on weighted variational regularized model. IEICE TRANSACTIONS on Information and Systems, 104(7), 961-969.
[8]. Zhou, H., Zhang, Z., Liu, Y., Xuan, M., Jiang, W., & Xiong, H. (2021). Single Image Dehazing Algorithm Based on Modified Dark Channel Prior. IEICE TRANSACTIONS on Information and Systems, 104(10), 1758-1761.
[9]. Granell, E., Chammas, E., Likforman-Sulem, L., Martínez-Hinarejos, C. D., Mokbel, C., & Cîrstea, B. I. (2018). Transcription of spanish historical handwritten documents with deep neural networks. Journal of Imaging, 4(1), 15.
[10]. Wang, M., & Deng, W. (2022). Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms. arXiv preprint arXiv:2205.09442.
[11]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
[12]. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
[13]. Breiman, L. (1996). Bagging predictors. Machine learning, 24, 123-140.
[14]. Zhou, Z. H. (2012). Ensemble methods: foundations and algorithms. CRC press.
[15]. Jiang, W. (2020). MNIST-MIX: a multi-language handwritten digit recognition dataset. IOP SciNotes, 1(2), 025002.
[16]. Jiang, W. (2020, May). Evaluation of deep learning models for Urdu handwritten characters recognition. In Journal of Physics: Conference Series (Vol. 1544, No. 1, p. 012016). IOP Publishing.
[17]. Jiang, W., & Zhang, L. (2020). Edge-siamnet and edge-triplenet: New deep learning models for handwritten numeral recognition. IEICE Transactions on Information and Systems, 103(3), 720-723.
[18]. Kibriya, H., Rafique, R., Ahmad, W., & Adnan, S. M. (2021, January). Tomato leaf disease detection using convolution neural network. In 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) (pp. 346-351). IEEE.
[19]. Flad, R. K. (2008). Divination and power: a multiregional view of the development of oracle bone divination in early China. Current Anthropology, 49(3), 403-437.
[20]. An, S., Lee, M., Park, S., Yang, H., & So, J. (2020). An ensemble of simple convolutional neural network models for MNIST digit recognition. arXiv preprint arXiv:2008.10400.
[21]. Jiang, W., & Luo, J. (2022). Graph neural network for traffic forecasting: A survey. Expert Systems with Applications, 117921.
[22]. Jiang, W. (2022). Graph-based deep learning for communication networks: A survey. Computer Communications, 185, 40-54.