# Open Source Handwritten Text Recognition on Medieval Manuscripts using Mixed Models and Document-Specific Finetuning

arXiv: https://arxiv.org/abs/2201.07661 · HTML: https://mlsanity.com/paper/2201.07661/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 19 Jan 2022.

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

- [Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition](https://mlsanity.com/paper/2005.13044/) (2020)
- [Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)](https://mlsanity.com/paper/2001.00139/) (2020)
- [Rethinking Text Line Recognition Models](https://mlsanity.com/paper/2104.07787/) (2021)
- [Calamari - A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition](https://mlsanity.com/paper/1807.02004/) (2018)
- [OCR4all -- An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings](https://mlsanity.com/paper/1909.04032/) (2019)
- [OCR of historical printings with an application to building diachronic corpora: A case study using the RIDGES herbal corpus](https://mlsanity.com/paper/1608.02153/) (2016)
- [Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting](https://mlsanity.com/paper/1711.09670/) (2017)
- [Mixed Model OCR Training on Historical Latin Script for Out-of-the-Box Recognition and Finetuning](https://mlsanity.com/paper/2106.07881/) (2021)
- [Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning](https://mlsanity.com/paper/1802.10038/) (2018)

## Built on by (most cited first, 4 of 4)

- [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)
- [New Results for the Text Recognition of Arabic Maghrib=i Manuscripts -- Managing an Under-resourced Script](https://mlsanity.com/paper/2211.16147/) (2022)
- [Practical Fine-Tuning of Autoregressive Models on Limited Handwritten Texts](https://mlsanity.com/paper/2503.19546/) (2025)

## Abstract (arXiv)

This paper deals with the task of practical and open source Handwritten Text Recognition (HTR) on German medieval manuscripts. We report on our efforts to construct mixed recognition models which can be applied out-of-the-box without any further document-specific training but also serve as a starting point for finetuning by training a new model on a few pages of transcribed text (ground truth). To train the mixed models we collected a corpus of 35 manuscripts and ca. 12.5k text lines for two widely used handwriting styles, Gothic and Bastarda cursives. Evaluating the mixed models out-of-the-box on four unseen manuscripts resulted in an average Character Error Rate (CER) of 6.22%. After training on 2, 4 and eventually 32 pages the CER dropped to 3.27%, 2.58%, and 1.65%, respectively. While the in-domain recognition and training of models (Bastarda model to Bastarda material, Gothic to Gothic) unsurprisingly yielded the best results, finetuning out-of-domain models to unseen scripts was still shown to be superior to training from scratch. Our new mixed models have been made openly available to the community.