@inproceedings{sanchez-carrera-etal-2024-unlocking,
    title = "Unlocking Knowledge with {OCR}-Driven Document Digitization for {P}eruvian Indigenous Languages",
    author = "Sanchez Carrera, Shadya  and
      Zariquiey, Roberto  and
      Oncevay, Arturo",
    editor = "Mager, Manuel  and
      Ebrahimi, Abteen  and
      Rijhwani, Shruti  and
      Oncevay, Arturo  and
      Chiruzzo, Luis  and
      Pugh, Robert  and
      von der Wense, Katharina",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.americasnlp-1.11/",
    doi = "10.18653/v1/2024.americasnlp-1.11",
    pages = "103--111",
    abstract = "The current focus on resource-rich languages poses a challenge to linguistic diversity, affecting minority languages with limited digital presence and relatively old published and unpublished resources. In addressing this issue, this study targets the digitalization of old scanned textbooks written in four Peruvian indigenous languages (Ash{\'a}ninka, Shipibo-Konibo, Yanesha, and Yine) using Optical Character Recognition (OCR) technology. This is complemented with text correction methods to minimize extraction errors. Contributions include the creation of an annotated dataset with 454 scanned page images, for a rigorous evaluation, and the development of a module to correct OCR-generated transcription alignments."
}