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Assessing advanced handwritten text recognition engines for digitizing historical documents.

C A Romein^1,2,3,4, A Rabus⁵, G Leifert⁶

¹Huygens Institute for the History and Culture of the Netherlands, Amsterdam, the Netherlands.

International Journal of Digital Humanities

|June 30, 2025

Summary

This study evaluates Handwritten Text Recognition (HTR) engines for digitizing historical documents. Titan and TrOCR-f excel with Latin scripts, while others perform well on non-Latin scripts after fine-tuning.

Keywords:

Digital humanities Handwritten text recognition (HTR)Historical document digitization IDA Language models Multilingual content Planet AI PyLaia TrOCR Transkribus

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Area of Science:

Digital Humanities
Computer Science
Information Science

Background:

Digitizing historical documents is crucial for accessibility and research.
Evaluating Handwritten Text Recognition (HTR) engine performance is essential for effective digital preservation.

Purpose of the Study:

To critically assess and compare the performance of leading HTR engines.
To evaluate their accuracy and efficiency across diverse historical document datasets.

Main Methods:

Comparative analysis of five state-of-the-art HTR engines: PyLaia, HTR+, IDA, TrOCR-f, and Titan.
Testing across datasets with varied scripts, styles, and historical orthography.

Main Results:

Titan and TrOCR-f demonstrated superior out-of-the-box performance for Latin-script documents.
PyLaia, IDA, and HTR+ showed strong performance on specific non-Latin scripts after fine-tuning.
All engines benefit significantly from training and fine-tuning, including language model integration.

Conclusions:

HTR engine selection depends on script and language requirements.
Effective digitization necessitates tailored training, fine-tuning, and language model integration.
This research provides insights for advancing HTR technology in digital humanities.