SEA-Vision：面向东南亚综合文档与场景文本理解的多语言基准
SEA-Vision: A Multilingual Benchmark for Comprehensive Document and Scene Text Understanding in Southeast Asia
该基准为多语言文档与场景文本理解提供了细粒度标注（页/块/行级）和五类推理的TEC-VQA，可用于评估多模态大模型在低资源语言上的性能退化，并推动文档解析与文本中心视觉问答的鲁棒性研究。
原文依据 We introduce SEA-Vision, a benchmark that jointly evaluates Document Parsing and Text-Centric Visual Question Answering (TEC-VQA) across 11 Southeast Asian languages.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多语言文档与场景文本理解在搜索、金融和公共服务等应用中发挥着重要作用。然而，现有基准大多聚焦于高资源语言，无法在真实的多语言环境中评估模型。在东南亚，语言多样性、复杂的书写系统以及高度多样的文档类型使这一挑战更加严峻。我们提出了SEA-Vision，一个联合评估11种东南亚语言的文档解析和文本中心视觉问答（TEC-VQA）的基准。SEA-Vision包含来自九种代表性文档类型的15,234个文档解析页面，标注了页级、块级和行级的层次化标签。它还提供了7,496个TEC-VQA问答对，用于探测文本识别、数值计算、比较分析、逻辑推理和空间理解。为了使这种多语言、多任务的标注可行，我们设计了一个用于文档解析和TEC-VQA的混合流水线。它结合了自动过滤和评分、多模态大模型辅助标注以及轻量级母语者验证，在保持高质量的同时大幅减少了人工标注。我们评估了多个领先的多模态模型，并观察到在低资源东南亚语言上性能显著下降，突显了多语言文档与场景文本理解中仍存在的巨大差距。我们相信SEA-Vision将有助于推动文档与场景文本理解的全球进展。
英文原文摘要 1537 字符
Multilingual document and scene text understanding plays an important role in applications such as search, finance, and public services. However, most existing benchmarks focus on high-resource languages and fail to evaluate models in realistic multilingual environments. In Southeast Asia, the diversity of languages, complex writing systems, and highly varied document types make this challenge even greater. We introduce SEA-Vision, a benchmark that jointly evaluates Document Parsing and Text-Centric Visual Question Answering (TEC-VQA) across 11 Southeast Asian languages. SEA-Vision contains 15,234 document parsing pages from nine representative document types, annotated with hierarchical page-, block-, and line-level labels. It also provides 7,496 TEC-VQA question-answer pairs that probe text recognition, numerical calculation, comparative analysis, logical reasoning, and spatial understanding. To make such multilingual, multi-task annotation feasible, we design a hybrid pipeline for Document Parsing and TEC-VQA. It combines automated filtering and scoring with MLLM-assisted labeling and lightweight native-speaker verification, greatly reducing manual labeling while maintaining high quality. We evaluate several leading multimodal models and observe pronounced performance degradation on low-resource Southeast Asian languages, highlighting substantial remaining gaps in multilingual document and scene text understanding. We believe SEA-Vision will help drive global progress in document and scene text understanding.