A Concise Survey of OCR for Low-Resource Languages

Milind Agarwal, Antonios Anastasopoulos

Abstract
Modern natural language processing (NLP) techniques increasingly require substantial amounts of data to train robust algorithms. Building such technologies for low-resource languages requires focusing on data creation efforts and data-efficient algorithms. For a large number of low-resource languages, especially Indigenous languages of the Americas, this data exists in image-based non-machine-readable documents. This includes scanned copies of comprehensive dictionaries, linguistic field notes, children’s stories, and other textual material. To digitize these resources, Optical Character Recognition (OCR) has played a major role but it comes with certain challenges in low-resource settings. In this paper, we share the first survey of OCR techniques specific to low-resource data creation settings and outline several open challenges, with a special focus on Indigenous Languages of the Americas. Based on experiences and results from previous research, we conclude with recommendations on utilizing and improving OCR for the benefit of computational researchers, linguists, and language communities.
Anthology ID:
2024.americasnlp-1.10
Volume:
Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Manuel Mager, Abteen Ebrahimi, Shruti Rijhwani, Arturo Oncevay, Luis Chiruzzo, Robert Pugh, Katharina von der Wense
Venues:
AmericasNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
88–102
Language:
URL:
https://aclanthology.org/2024.americasnlp-1.10
DOI:
Bibkey:
Cite (ACL):
Milind Agarwal and Antonios Anastasopoulos. 2024. A Concise Survey of OCR for Low-Resource Languages. In Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024), pages 88–102, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
A Concise Survey of OCR for Low-Resource Languages (Agarwal & Anastasopoulos, AmericasNLP-WS 2024)
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https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.americasnlp-1.10.pdf
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