1. Amrhein, C., Clematide, S.: Supervised OCR error detection and correction using statistical and neural machine translation methods. J. Lang. Technol. Comput. Linguist. (JLCL) 33(1), 49–76 (2018)
2. Belay, B., Habtegebrial, T., Meshesha, M., Liwicki, M., Belay, G., Stricker, D.: Amharic OCR: an end-to-end learning. Appl. Sci. 10(3), 1117 (2020)
3. Breuel, T.M., Ul-Hasan, A., Al-Azawi, M.A., Shafait, F.: High-performance OCR for printed English and Fraktur using LSTM networks. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 683–687. IEEE (2013)
4. Chen, Y.H., Zhou, Y.: Enhancing OCR Performance Through Post-OCR Models: Adopting Glyph Embedding for Improved Correction. arXiv preprint arXiv:2308.15262 (2023)
5. Devlin, J., Chang, M-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T., (eds) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186 (2019)