# Optical Character Recognition of 19th Century Classical Commentaries: the Current State of Affairs

arXiv: https://arxiv.org/abs/2110.06817 · HTML: https://mlsanity.com/paper/2110.06817/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Calamari - A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition](https://mlsanity.com/paper/1807.02004/) (2018)
- [Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin](https://mlsanity.com/paper/1809.05501/) (2018)
- [OCR4all -- An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings](https://mlsanity.com/paper/1909.04032/) (2019)

## Built on by (most cited first, 7 of 7)

- [Yes but.. Can ChatGPT Identify Entities in Historical Documents?](https://mlsanity.com/paper/2303.17322/) (2023)
- [CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition](https://mlsanity.com/paper/2509.19768/) (2025)
- [Ancient Script Image Recognition and Processing: A Review](https://mlsanity.com/paper/2506.19208/) (2025)
- [CLOCR-C: Context Leveraging OCR Correction with Pre-trained Language Models](https://mlsanity.com/paper/2408.17428/) (2024)
- [Reading or Guessing? Visual Grounding Failures of Vision-Language Models for OCR in Ancient Greek Editions](https://mlsanity.com/paper/2605.27750/) (2026)
- [Making Old Kurdish Publications Processable by Augmenting Available Optical Character Recognition Engines](https://mlsanity.com/paper/2404.06101/) (2024)
- [The Patrologia Graeca Corpus: OCR, Annotation, and Open Release of Noisy Nineteenth-Century Polytonic Greek Editions](https://mlsanity.com/paper/2603.09470/) (2026)

## Abstract (arXiv)

Together with critical editions and translations, commentaries are one of the main genres of publication in literary and textual scholarship, and have a century-long tradition. Yet, the exploitation of thousands of digitized historical commentaries was hitherto hindered by the poor quality of Optical Character Recognition (OCR), especially on commentaries to Greek texts. In this paper, we evaluate the performances of two pipelines suitable for the OCR of historical classical commentaries. Our results show that Kraken + Ciaconna reaches a substantially lower character error rate (CER) than Tesseract/OCR-D on commentary sections with high density of polytonic Greek text (average CER 7% vs. 13%), while Tesseract/OCR-D is slightly more accurate than Kraken + Ciaconna on text sections written predominantly in Latin script (average CER 8.2% vs. 8.4%). As part of this paper, we also release GT4HistComment, a small dataset with OCR ground truth for 19th classical commentaries and Pogretra, a large collection of training data and pre-trained models for a wide variety of ancient Greek typefaces.