INDOTABVQA: A Benchmark for Cross-Lingual Table Understanding in Bahasa Indonesia Documents

We introduce INDOTABVQA, a benchmark for evaluating cross-lingual Table Visual Question Answering (VQA) on real-world document images in Bahasa Indonesia.The dataset comprises 1,593 document images across three visual styles (bordered, borderless, and colorful) with one or more than one tables, and 1,593 question-answer sets in four languages: Bahasa Indonesia, English, Hindi, and Arabic.This enables evaluation of Vision-Language Models (VLMs) in both monolingual (Bahasa documents with Bahasa questions) and cross-lingual settings (Bahasa documents with questions in other languages).We benchmark leading open-source VLMs (Qwen2.5-VL,Gemma-3, LLaMA-3.2) and GPT-4o and reveal substantial performance gaps, particularly on structurally complex tables and in lowresource languages.Fine-tuning a compact 3B model and a LoRA-finetuned 7B model on our dataset yields 11.6% and 17.8% improvements in accuracy.Providing explicit table region coordinates as additional input further improves performance by 4-7%, demonstrating the value of Spatial priors for table-based reasoning.Our findings underscore the importance of language-diverse, domain-specific datasets and demonstrate that targeted finetuning can significantly enhance VLM performance on specialized document understanding tasks.INDOTABVQA provides a valuable resource for advancing research in crosslingual, structure-aware document understanding, especially in underrepresented regions of the world.The dataset is publicly available via Hugging Face at