NusaAksara: A Multimodal and Multilingual Benchmark for Preserving Indonesian Indigenous Scripts
This addresses the preservation of Indonesian indigenous scripts for NLP researchers and communities, but it is incremental as it focuses on creating a benchmark rather than a new method.
The paper tackles the problem of limited NLP progress for Indonesian indigenous scripts by introducing NusaAksara, a multimodal and multilingual benchmark covering 8 scripts across 7 languages, including low-resource ones, and shows that most NLP technologies achieve near-zero performance on these scripts.
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.