# Important New Developments in Arabographic Optical Character Recognition (OCR)

arXiv: https://arxiv.org/abs/1703.09550 · HTML: https://mlsanity.com/paper/1703.09550/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2017.

## Built on by (most cited first, 6 of 6)

- [Reading or Guessing? Visual Grounding Failures of Vision-Language Models for OCR in Ancient Greek Editions](https://mlsanity.com/paper/2605.27750/) (2026)
- [Lacuna Reconstruction: Self-supervised Pre-training for Low-Resource Historical Document Transcription](https://mlsanity.com/paper/2112.08692/) (2021)
- [Handling Heavily Abbreviated Manuscripts: HTR engines vs text normalisation approaches](https://mlsanity.com/paper/2107.03450/) (2021)
- [New Results for the Text Recognition of Arabic Maghrib=i Manuscripts -- Managing an Under-resourced Script](https://mlsanity.com/paper/2211.16147/) (2022)
- [Advances and Limitations in Open Source Arabic-Script OCR: A Case Study](https://mlsanity.com/paper/2402.10943/) (2024)
- [VOLTAGE: A Versatile Contrastive Learning based OCR Methodology for ultra low-resource scripts through Auto Glyph Feature Extraction](https://mlsanity.com/paper/2510.10490/) (2025)

## Abstract (arXiv)

The OpenITI team has achieved Optical Character Recognition (OCR) accuracy rates for classical Arabic-script texts in the high nineties. These numbers are based on our tests of seven different Arabic-script texts of varying quality and typefaces, totaling over 7,000 lines. These accuracy rates not only represent a distinct improvement over the actual accuracy rates of the various proprietary OCR options for classical Arabic-script texts, but, equally important, they are produced using an open-source OCR software, thus enabling us to make this Arabic-script OCR technology freely available to the broader Islamic, Persian, and Arabic Studies communities.