@inproceedings{adilazuarda-etal-2025-nusaaksara,
    title = "{N}usa{A}ksara: A Multimodal and Multilingual Benchmark for Preserving {I}ndonesian Indigenous Scripts",
    author = "Adilazuarda, Muhammad Farid  and
      Wijanarko, Musa Izzanardi  and
      Susanto, Lucky  and
      Nur{'}aini, Khumaisa  and
      Wijaya, Derry Tanti  and
      Aji, Alham Fikri",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1377/",
    pages = "28371--28401",
    ISBN = "979-8-89176-251-0",
    abstract = "Indonesia is rich in languages and scripts. However, most NLP progress has been made using romanized text. In this paper, we present NusaAksara, a novel public benchmark for Indonesian languages that includes their original scripts. Our benchmark covers both text and image modalities and encompasses diverse tasks such as image segmentation, OCR, transliteration, translation, and language identification. Our data is constructed by human experts through rigorous steps. NusaAksara covers 8 scripts across 7 languages, including low-resource languages not commonly seen in NLP benchmarks. Although unsupported by Unicode, the Lampung script is included in this dataset. We benchmark our data across several models, from LLMs and VLMs such as GPT-4o, Llama 3.2, and Aya 23 to task-specific systems such as PP-OCR and LangID, and show that most NLP technologies cannot handle Indonesia{'}s local scripts, with many achieving near-zero performance."
}