The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
TrOCR: Transformer-Based Optical Character Recognition with Pre-trained Models
Open accessAbstract
Text recognition is a long-standing research problem for document digitalization. Existing approaches are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments show that the TrOCR model outperforms the current state-of-the-art models on the printed, handwritten and scene text recognition tasks. The TrOCR models and code are publicly available at https://aka.ms/trocr.
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D., Zoph, B., Shlens, J., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00359 · Cited by 3,223
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Zero-shot text-to-image generation
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fairseq: A Fast, Extensible Toolkit for Sequence Modeling
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Cited by
Benchmarking large language models for handwritten text recognition
Crosilla, G., Klic, L., & Colavizza, G. (2025) · Journal of Documentation · doi:10.1108/jd-03-2025-0082 · Cited by 34
GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions
Hsu, T.-Y., Huang, C.-Y., Rossi, R., Kim, S., Giles, C., & Huang, T.-H. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.363 · Cited by 8
Human-in-the-loop tabular data extraction methods for historical climate data rescue
Vercruysse, B., Birkholz, J. M., Chandrasekar, K. K. T., Muheki, D., Thiery, W., Verbeeck, H., Hufkens, K., Jacobsen, K., & Verbruggen, C. (2026) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-025-00524-y · Cited by 5
Metadata
- Authors
- Li, M., Lv, T., Chen, J., Cui, L., Lu, Y., Florencio, D., Zhang, C., Li, Z., & Wei, F.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2023-06-26
- Volume
- 37
- Issue
- 11
- Pages
- 13094-13102
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4382202677
- Cited by
- 573
- References
- 64
- FWCI
- 64.24
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Li, M., Lv, T., Chen, J., Cui, L., Lu, Y., Florencio, D., Zhang, C., Li, Z., & Wei, F. (2023). TrOCR: Transformer-Based Optical Character Recognition with Pre-trained Models. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v37i11.26538
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