# Transformer-based HTR for Historical Documents

arXiv: https://arxiv.org/abs/2203.11008 · HTML: https://mlsanity.com/paper/2203.11008/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [Rethinking Text Line Recognition Models](https://mlsanity.com/paper/2104.07787/) (2021)

## Built on by (most cited first, 10 of 10)

- [Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents](https://mlsanity.com/paper/2504.00414/) (2025)
- [Benchmarking Large Language Models for Handwritten Text Recognition](https://mlsanity.com/paper/2503.15195/) (2025)
- [Extending TrOCR for Text Localization-Free OCR of Full-Page Scanned Receipt Images](https://mlsanity.com/paper/2212.05525/) (2022)
- [A tailored Handwritten-Text-Recognition System for Medieval Latin](https://mlsanity.com/paper/2308.09368/) (2023)
- [Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI](https://mlsanity.com/paper/2310.09091/) (2023)
- [Handwritten Text Recognition of Historical Manuscripts Using Transformer-Based Models](https://mlsanity.com/paper/2508.11499/) (2025)
- [The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique](https://mlsanity.com/paper/2212.11146/) (2022)
- [Application of deep learning approaches for medieval historical documents transcription](https://mlsanity.com/paper/2512.18865/) (2025)
- [Adapting TrOCR for Printed Tigrinya Text Recognition: Word-Aware Loss Weighting for Cross-Script Transfer Learning](https://mlsanity.com/paper/2604.20813/) (2026)
- [ICDAR 2026 HIPE-OCRepair Competition on LLM-Assisted OCR Post-Correction for Historical Documents](https://mlsanity.com/paper/2607.08143/) (2026)

## 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.