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TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
Minghao Li, Tengchao Lv, Jingye Chen et al., 2023. Proceedings of AAAI 2023.
Presents TrOCR, a text recognition model that pairs a pre-trained image Transformer encoder with a pre-trained text Transformer decoder.
What it's used for here
Cited in the emerging-methods survey's section on handwritten text recognition.
Cited by on this site 1 record
1 methods surveys
SRCMathematics
Cite it
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 AAAI 2023. https://arxiv.org/abs/2109.10282
@article{li2023trocr,
author = {Li, Minghao and Lv, Tengchao and Chen, Jingye and Cui, Lei and Lu, Yijuan and Florencio, Dinei and Zhang, Cha and Li, Zhoujun and Wei, Furu},
title = {TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models},
journal = {Proceedings of AAAI 2023},
year = {2023},
url = {https://arxiv.org/abs/2109.10282}
}TY - JOUR TI - TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models AU - Li, Minghao AU - Lv, Tengchao AU - Chen, Jingye AU - Cui, Lei AU - Lu, Yijuan AU - Florencio, Dinei AU - Zhang, Cha AU - Li, Zhoujun AU - Wei, Furu PY - 2023 JO - Proceedings of AAAI 2023 UR - https://arxiv.org/abs/2109.10282 ER -
Li, Minghao et al.. “TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models.” Proceedings of AAAI 2023 (2023).