# Comparative analysis of optical character recognition methods for S'ami texts from the National Library of Norway

arXiv: https://arxiv.org/abs/2501.07300 · HTML: https://mlsanity.com/paper/2501.07300/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 13 Jan 2025.

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

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model](https://mlsanity.com/paper/2409.01704/) (2024)
- [Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)](https://mlsanity.com/paper/2001.00139/) (2020)
- [An Unsupervised method for OCR Post-Correction and Spelling Normalisation for Finnish](https://mlsanity.com/paper/2011.03502/) (2020)
- [Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library](https://mlsanity.com/paper/2404.18722/) (2024)
- [A Tool for Facilitating OCR Postediting in Historical Documents](https://mlsanity.com/paper/2004.11471/) (2020)
- [User-Centric Evaluation of OCR Systems for Kwak'wala](https://mlsanity.com/paper/2302.13410/) (2023)
- [Making Old Kurdish Publications Processable by Augmenting Available Optical Character Recognition Engines](https://mlsanity.com/paper/2404.06101/) (2024)

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

- [Stringalign: Moving beyond summary statistics with a transparent Unicode-aware tool for evaluating automatic transcription models](https://mlsanity.com/paper/2606.16015/) (2026)

## Abstract (arXiv)

Optical Character Recognition (OCR) is crucial to the National Library of Norway's (NLN) digitisation process as it converts scanned documents into machine-readable text. However, for the S'ami documents in NLN's collection, the OCR accuracy is insufficient. Given that OCR quality affects downstream processes, evaluating and improving OCR for text written in S'ami languages is necessary to make these resources accessible. To address this need, this work fine-tunes and evaluates three established OCR approaches, Transkribus, Tesseract and TrOCR, for transcribing S'ami texts from NLN's collection. Our results show that Transkribus and TrOCR outperform Tesseract on this task, while Tesseract achieves superior performance on an out-of-domain dataset. Furthermore, we show that fine-tuning pre-trained models and supplementing manual annotations with machine annotations and synthetic text images can yield accurate OCR for S'ami languages, even with a moderate amount of manually annotated data.