# MMTIT-Bench: A Multilingual and Multi-Scenario Benchmark with Cognition-Perception-Reasoning Guided Text-Image Machine Translation

arXiv: https://arxiv.org/abs/2603.23896 · HTML: https://mlsanity.com/paper/2603.23896/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 25 of 25)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [Demystifying Long Chain-of-Thought Reasoning in LLMs](https://mlsanity.com/paper/2502.03373/) (2025)
- [Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers](https://mlsanity.com/paper/2506.23918/) (2025)
- [General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model](https://mlsanity.com/paper/2409.01704/) (2024)
- [Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models](https://mlsanity.com/paper/2402.14207/) (2024)
- [MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing](https://mlsanity.com/paper/2509.22186/) (2025)
- [MiMo-VL Technical Report](https://mlsanity.com/paper/2506.03569/) (2025)
- [M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding](https://mlsanity.com/paper/2411.04952/) (2024)
- [Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models](https://mlsanity.com/paper/2312.06109/) (2023)
- [dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model](https://mlsanity.com/paper/2512.02498/) (2025)
- [Exploring Better Text Image Translation with Multimodal Codebook](https://mlsanity.com/paper/2305.17415/) (2023)
- [Towards Fully Automated Manga Translation](https://mlsanity.com/paper/2012.14271/) (2020)
- [Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task](https://mlsanity.com/paper/2210.03887/) (2022)
- [Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation](https://mlsanity.com/paper/2401.06568/) (2024)
- [Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation](https://mlsanity.com/paper/2407.02894/) (2024)
- [MT3: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2505.19714/) (2025)
- [Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency](https://mlsanity.com/paper/2507.08309/) (2025)
- [Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation](https://mlsanity.com/paper/2507.07572/) (2025)
- [PATIMT-Bench: A Multi-Scenario Benchmark for Position-Aware Text Image Machine Translation in Large Vision-Language Models](https://mlsanity.com/paper/2509.12278/) (2025)
- [Automatic Evaluation Metrics for Document-level Translation: Overview, Challenges and Trends](https://mlsanity.com/paper/2504.14804/) (2025)

## Built on by (most cited first, 4 of 4)

- [HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better](https://mlsanity.com/paper/2607.04884/) (2026)
- [Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters](https://mlsanity.com/paper/2605.11960/) (2026)
- [MORE: A Multilingual Document Parsing Benchmark and Evaluation](https://mlsanity.com/paper/2607.02956/) (2026)
- [UniTranslator: A Unified Multi-modal Framework for End-to-end In-Image Machine Translation](https://mlsanity.com/paper/2606.24333/) (2026)

## Abstract (arXiv)

End-to-end text-image machine translation (TIMT), which directly translates textual content in images across languages, is crucial for real-world multilingual scene understanding. Despite advances in vision-language large models (VLLMs), robustness across diverse visual scenes and low-resource languages remains underexplored due to limited evaluation resources. We present MMTIT-Bench, a human-verified multilingual and multi-scenario benchmark with 1,400 images spanning fourteen non-English and non-Chinese languages and diverse settings such as documents, scenes, and web images, enabling rigorous assessment of end-to-end TIMT. Beyond benchmarking, we study how reasoning-oriented data design improves translation. Although recent VLLMs have begun to incorporate long Chain-of-Thought (CoT) reasoning, effective thinking paradigms for TIMT are still immature: existing designs either cascade parsing and translation in a sequential manner or focus on language-only reasoning, overlooking the visual cognition central to VLLMs. We propose Cognition-Perception-Reasoning for Translation (CPR-Trans), a data paradigm that integrates scene cognition, text perception, and translation reasoning within a unified reasoning process. Using a VLLM-driven data generation pipeline, CPR-Trans provides structured, interpretable supervision that aligns perception with reasoning. Experiments on 3B and 7B models show consistent gains in accuracy and interpretability. We will release MMTIT-Bench to promote the multilingual and multi-scenario TIMT research upon acceptance.