Comparative analysis of optical character recognition methods for Sámi texts from the National Library of Norway
This addresses the need to improve digitization and accessibility of Sámi language documents for cultural preservation, though it is incremental as it applies existing methods to a new dataset.
This work tackled the problem of low OCR accuracy for Sámi texts in the National Library of Norway's collection by fine-tuning and evaluating three OCR methods, finding that Transkribus and TrOCR outperformed Tesseract on this specific task.
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 Sámi 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 Sámi 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 Sámi 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 Sámi languages, even with a moderate amount of manually annotated data.