1. Shi B., Bai X., Yao C.. “An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39:11 (2017), pp. 2298-2304. DOI: 10.1109/TPAMI.2016.2646371 arXiv: 1507.05717
2. Hochreiter S., Schmidhuber J.. “Long short-term memory”, Neural Computation, 9:8 (1997), pp. 1735-1780. DOI: 10.1162/neco.1997.9.8.1735
3. Chung J., Gulcehre C., Cho K., Bengio Y.. “Gated feedback recurrent neural networks”, Proceedings of Machine Learning Research, 37 (2015), pp. 2067-2075. arXiv: 1502.02367
4. Винокуров И. В.. «Использование свёрточной нейронной сети для распознавания элементов текста на отсканированных изображениях плохого качества», Программные системы: теория и приложения, 13:3(54) (2022), с. 29-43. DOI: 10.25209/2079-3316-2022-13-3-29-43 EDN: MWQQAW
5. Винокуров И. В.. «Распознавание табличной информации с использованием свёрточных нейронных сетей», Программные системы: теория и приложения, 14:1(56) (2023), с. 3-30. DOI: 10.25209/2079-3316-2023-14-1-3-30 EDN: UEYQVH
6. Винокуров И. В.. «Распознавание цифровых последовательностей с использованием свёрточных нейронных сетей», Программные системы: теория и приложения, 14:3(58) (2023), с. 3-36. DOI: 10.25209/2079-3316-2023-14-3-3-36 EDN: JKKZXL
7. He P., Huang W., Qiao Y., Change Loy C., Tang X.. “Reading scene text in deep convolutional sequences”, AAAI’16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (Phoenix, Arizona, USA, February 12-17, 2016), Proceedings of the AAAI Conference on Artificial Intelligence, 30 (2016), pp. 3501-3508. DOI: 10.1609/aaai.v30i1.10465
8. Shi B., Wang X., Lv P., Yao C., Bai X.. “Robust scene text recognition with automatic rectification”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Las Vegas, NV, USA, June 27-30, 2016), 2016, pp. 4168-4176. DOI: 10.1109/CVPR.2016.452 arXiv: 1603.03915
9. Yin F., Wu Y. -C., Zhang X. -Y., Liu C. -L.. Scene text recognition with sliding convolutional character models, 2017, 10 pp. arXiv: 1709.01727
10. Nirmalasari D. A., Suciati N., Navastara D. A.. “Handwritten text recognition using fully convolutional network”, IOP Conference Series: Materials Science and Engineering, 1077:1 (2021), 012030, 9 pp. DOI: 10.1088/1757-899X/1077/1/012030 EDN: PCGTBA
11. Liu X., Deng Y., Sun Y., Zhou Y.. “Multi-digit recognition with convolutional neural network and long short-term memory”, 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (Huangshan, China, July 28-30, 2018), IEEE, 2018, pp. 1187-1192. DOI: 10.1109/FSKD.2018.8686963
12. Madakannu A., Selvaraj A.. “DIGI-Net: a deep convolutional neural network for multi-format digit recognition”, Neural Computing and Applications, 32 (2020), pp. 11373-11383. DOI: 10.1007/s00521-019-04632-9 EDN: NCKUQQ
13. Zou L., He Z., Wang K., Wu Z., Wang Y., Zhang G., Wang X.. “Text recognition model based on multi-scale fusion CRNN”, Sensors, 32:16 (2023), 7034, 18 pp. DOI: 10.3390/s23167034 EDN: QPPIKP
14. Agrawal V., Jagtap J.. Convolutional vision transformer for handwritten digit recognition, Research Square, 2022, 11 pp. DOI: 10.21203/rs.3.rs-1984839/v1
15. Cheng L., Khalitov R., Yu T., Yang Z.. Classification of long sequential data using circular dilated convolutional neural networks, 2022, 16 pp. arXiv: 2201.02143
16. Bhat R. S.. Text recognition with CRNN-CTC network, W&B Fully Connected, 2022 URL https://wandb.ai/authors/text-recognition-crnn-ctc/reports/Text-Recognition-With-CRNN-CTC-Network-VmlldzoxNTI5NDI.
17. Khamekhem S., Sourour A., Kessentini Y.. “Domain and writer adaptation of offline Arabic handwriting recognition using deep neural networks”, Neural Computing and Applications, 34 (2022), pp. 2055-2071. DOI: 10.1007/s00521-021-06520-7 EDN: FGGXHL