# Boosting offline handwritten text recognition in historical documents with few labeled lines

arXiv: https://arxiv.org/abs/2012.02544 · HTML: https://mlsanity.com/paper/2012.02544/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 19 of 19)

- [The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition](https://mlsanity.com/paper/2208.07682/) (2022)
- [Boosting Modern and Historical Handwritten Text Recognition with Deformable Convolutions](https://mlsanity.com/paper/2208.08109/) (2022)
- [How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuning](https://mlsanity.com/paper/2305.02593/) (2023)
- [Handwritten Text Recognition: A Survey](https://mlsanity.com/paper/2502.08417/) (2025)
- [KOHTD: Kazakh Offline Handwritten Text Dataset](https://mlsanity.com/paper/2110.04075/) (2021)
- [On the Generalization of Handwritten Text Recognition Models](https://mlsanity.com/paper/2411.17332/) (2024)
- [Towards Writing Style Adaptation in Handwriting Recognition](https://mlsanity.com/paper/2302.06318/) (2023)
- [Fine-tuning Is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition](https://mlsanity.com/paper/2302.06308/) (2023)
- [Crossing Points Detection in Plain Weave for Old Paintings with Deep Learning](https://mlsanity.com/paper/2302.11924/) (2023)
- [Npix2Cpix: A GAN-Based Image-to-Image Translation Network With Retrieval- Classification Integration for Watermark Retrieval From Historical Document Images](https://mlsanity.com/paper/2406.03556/) (2024)
- [StylusAI: Stylistic Adaptation for Robust German Handwritten Text Generation](https://mlsanity.com/paper/2407.15608/) (2024)
- [Quo Vadis Handwritten Text Generation for Handwritten Text Recognition?](https://mlsanity.com/paper/2508.09936/) (2025)
- [Handwritten text generation and strikethrough characters augmentation](https://mlsanity.com/paper/2112.07395/) (2021)
- [Thread Counting in Plain Weave for Old Paintings Using Semi-Supervised Regression Deep Learning Models](https://mlsanity.com/paper/2303.15999/) (2023)
- [Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition](https://mlsanity.com/paper/2404.11585/) (2024)
- [Practical Fine-Tuning of Autoregressive Models on Limited Handwritten Texts](https://mlsanity.com/paper/2503.19546/) (2025)
- [Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies](https://mlsanity.com/paper/2604.09713/) (2026)
- [Leveraging Morphology for Historical Script Metrological Analysis](https://mlsanity.com/paper/2606.09446/) (2026)
- [Handwritten Text Recognition Lives in the High-Pixel Variance Subspace](https://mlsanity.com/paper/2609.35473/) (2026)

## Abstract (arXiv)

In this paper, we face the problem of offline handwritten text recognition (HTR) in historical documents when few labeled samples are available and some of them contain errors in the train set. Three main contributions are developed. First we analyze how to perform transfer learning (TL) from a massive database to a smaller historical database, analyzing which layers of the model need a fine-tuning process. Second, we analyze methods to efficiently combine TL and data augmentation (DA). Finally, an algorithm to mitigate the effects of incorrect labelings in the training set is proposed. The methods are analyzed over the ICFHR 2018 competition database, Washington and Parzival. Combining all these techniques, we demonstrate a remarkable reduction of CER (up to 6% in some cases) in the test set with little complexity overhead.