@inproceedings{agarwal-anastasopoulos-2025-ailla,
    title = "{AILLA}-{OCR}: A First Textual and Structural Post-{OCR} Dataset for 8 Indigenous Languages of {L}atin {A}merica",
    author = "Agarwal, Milind  and
      Anastasopoulos, Antonios",
    editor = "Lachler, Jordan  and
      Agyapong, Godfred  and
      Arppe, Antti  and
      Moeller, Sarah  and
      Chaudhary, Aditi  and
      Rijhwani, Shruti  and
      Rosenblum, Daisy",
    booktitle = "Proceedings of the Eight Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2025",
    address = "Honolulu, Hawaii, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.computel-main.13/",
    pages = "120--127",
    abstract = "It is by now common knowledge in the NLP community that low-resource languages need large-scale data creation efforts and novel con- tributions in the form of robust algorithms that work in data-scarce settings. Amongst these languages, however, many have a large amount of data, ripe for NLP applications, except that this data exists in image-based formats. This includes scanned copies of extremely valuable dictionaries, linguistic field notes, children{'}s stories, plays, and other textual material. To extract the text data from these non machine- readable images, Optical Character Recogni- tion (OCR) is the most popular technique, but it has proven to be challenging for low-resource languages because of their unique properties (uncommon diacritics, rare words etc.) and due to a general lack of preserved page-structure in the OCR output. So, to contribute to the reduc- tion of these two big bottlenecks (lack of text data and layout quality), we release the first textual and structural OCR dataset for 8 indige- nous languages of Latin America. We hope that our dataset will encourage researchers within the NLP and Computational Linguistics com- munities to work with these languages."
}