NusaAksara: A Multimodal and Multilingual Benchmark for Preserving Indonesian Indigenous Scripts

ACL ARR 2025 February Submission2915 Authors

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.

Paper Type: Long

Research Area: Multilingualism and Cross-Lingual NLP

Research Area Keywords: multilingual benchmarks, endangered languages, resources for less-resourced languages, indigenous languages

Contribution Types: Data resources

Languages Studied: Lampungnese, Balinese, Bataknese, Javanese, Buginese, Sundanese, Malay

Submission Number: 2915