mlsanityData to 7 Oct 2026

Transformer-based HTR for Historical Documents

arXiv 2203.11008 ↗First posted 21 Mar 2022cs.CV cs.CLcited by 10

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Abstract (arXiv)

We apply the TrOCR framework to real-world, historical manuscripts and show that TrOCR per se is a strong model, ideal for transfer learning. TrOCR has been trained on English only, but it can adapt to other languages that use the Latin alphabet fairly easily and with little training material. We compare TrOCR against a SOTA HTR framework (Transkribus) and show that it can beat such systems. This finding is essential since Transkribus performs best when it has access to baseline information, which is not needed at all to fine-tune TrOCR.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 21 Mar 2022.

Builds on

9 of 17 references.

References · 9

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  5. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  6. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  7. 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,360
  8. 2021TrOCR: Transformer-based Optical Character Recognition with Pre-trained Modelscited by 278
  9. 2021Rethinking Text Line Recognition Modelscited by 45

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 4; 2026: 2.

10 papers.