How Robust is OCR-Reasoning? Evaluating OCR-Reasoning Robustness of Vision-Language Models under Visual Perturbations
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China（教育部知识驱动人机智能工程研究中心）；International Center of Future Science, Jilin University（吉林大学未来科学国际合作中心）
Vision-language models (VLMs) have achieved strong performance on OCR-based benchmarks and increasingly focused on text-rich understanding, but their robustness under controlled visual degradation remains insufficiently understood. This gap is critical for OCR reasoning, where visual corruption can induce OCR errors and structural distortions, thereby introducing uncertainty into the reasoning task. To systematically study this problem, we introduce OCR-Robust, a benchmark designed for evaluating OCR reasoning robustness under visual perturbations. It contains 812 samples across two complementary subsets: OCR1.0, covering documents, scene text, receipts, handwriting, and mathematical content, and OCR2.0, focusing on charts, geometry diagrams, and tables. To enable efficient yet informative evaluation, we conduct a pilot study over 18 candidate perturbations and select 5 representative types at 3 severity levels each based on their impact and cross-model discriminability. We evaluate robustness using clean accuracy, Relative Corruption Retention (RCR), Worst-Case Retention (WCR), and a composite Corruption Robustness Index (CRI), and benchmark 18 models spanning proprietary systems, open-source VLMs, and OCR+LLM pipelines. Our results show that higher clean accuracy does not necessarily imply stronger robustness, and that models can suffer pronounced degradation in the worst case on OCR tasks that are sensitive to structure, and charts and tables are substantially more fragile than document-like inputs under perturbation.
Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）；Sun Yat-Sen University（中山大学）；Chinese University of Hong Kong（香港中文大学）
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Information and Communication Engineering, Communication University of China（中国传媒大学信息与通信工程学院）；Heilongjiang Network Space Research Center（黑龙江省网络空间研究中心）
Reasoning-OCR: Can Large Multimodal Models Solve Complex Logical Reasoning Problems from OCR Cues?
School of Computer Science, National Engineering Research Center for Multimedia Software, and Institute of Artificial Intelligence, Wuhan University, China（计算机学院、多媒体软件国家工程研究中心及人工智能研究所、武汉大学）；College of Computing & Data Science at Nanyang Technological University（南洋理工大学计算与数据科学学院）
Evaluating Vision-Language Models as a Zero-Shot Learning Alternative to You Only Look Once and Optical Character Recognition for Nigerian License Plate Recognition
Ahmadu Bello University（艾哈迈杜·贝罗大学）；Bayero University Kano（卡诺贝罗大学）
Tsinghua University（清华大学）；Meituan Inc（美团公司）；Central South University（中南大学）；Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Nanyang Technological University（南洋理工大学）
RUArt: A Novel Text-Centered Solution for Text-Based Visual Question Answering
University of Science and Technology Beijing（北京科技大学）；Tencent Technology (Shenzhen) Company Limited（腾讯科技（深圳）有限公司）；USTB-EEasyTech Joint Laboratory of Artificial Intelligence（北京科技大学-EEasyTech人工智能联合实验室）；School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院计算机科学与技术系）；Department of Industrial Design, School of Mechanical Engineering, University of Science and Technology Beijing（北京科技大学机械工程学院工业设计系）；Institute of Artificial Intelligence, University of Science and Technology Beijing（北京科技大学人工智能研究院）
OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation
Shanghai AI Laboratory（上海人工智能实验室）；Peking University（北京大学）；The University of HongKong（香港大学）；Shanghai Jiaotong University（上海交通大学）；Beihang University（北京航空航天大学）
