# Making Old Kurdish Publications Processable by Augmenting Available Optical Character Recognition Engines

arXiv: https://arxiv.org/abs/2404.06101 · HTML: https://mlsanity.com/paper/2404.06101/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin](https://mlsanity.com/paper/1809.05501/) (2018)
- [Leveraging Multilingual News Websites for Building a Kurdish Parallel Corpus](https://mlsanity.com/paper/2010.01554/) (2020)
- [Optical Character Recognition of 19th Century Classical Commentaries: the Current State of Affairs](https://mlsanity.com/paper/2110.06817/) (2021)
- [State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines](https://mlsanity.com/paper/1810.03436/) (2018)
- [A Tool for Facilitating OCR Postediting in Historical Documents](https://mlsanity.com/paper/2004.11471/) (2020)
- [An end-to-end Optical Character Recognition approach for ultra-low-resolution printed text images](https://mlsanity.com/paper/2105.04515/) (2021)

## Built on by (most cited first, 1 of 1)

- [Comparative analysis of optical character recognition methods for S'ami texts from the National Library of Norway](https://mlsanity.com/paper/2501.07300/) (2025)

## Abstract (arXiv)

Kurdish libraries have many historical publications that were printed back in the early days when printing devices were brought to Kurdistan. Having a good Optical Character Recognition (OCR) to help process these publications and contribute to the Kurdish languages resources which is crucial as Kurdish is considered a low-resource language. Current OCR systems are unable to extract text from historical documents as they have many issues, including being damaged, very fragile, having many marks left on them, and often written in non-standard fonts and more. This is a massive obstacle in processing these documents as currently processing them requires manual typing which is very time-consuming. In this study, we adopt an open-source OCR framework by Google, Tesseract version 5.0, that has been used to extract text for various languages. Currently, there is no public dataset, and we developed our own by collecting historical documents from Zheen Center for Documentation and Research, which were printed before 1950 and resulted in a dataset of 1233 images of lines with transcription of each. Then we used the Arabic model as our base model and trained the model using the dataset. We used different methods to evaluate our model, Tesseracts built-in evaluator lstmeval indicated a Character Error Rate (CER) of 0.755%. Additionally, Ocreval demonstrated an average character accuracy of 84.02%. Finally, we developed a web application to provide an easy- to-use interface for end-users, allowing them to interact with the model by inputting an image of a page and extracting the text. Having an extensive dataset is crucial to develop OCR systems with reasonable accuracy, as currently, no public datasets are available for historical Kurdish documents; this posed a significant challenge in our work. Additionally, the unaligned spaces between characters and words proved another challenge with our work.