arXiv Computation and Language
ImgMT: Wikipedia-based Dataset For Evaluating Text-in-Image Translation
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VISTA-Bench: Benchmarking Multilingual Image Translation with Image-Specific Rubrics
arXiv:2609.37287v1 Announce Type: cross Abstract: Image translation is a fundamental capability of multimodal models for multilingual applications, requiring visual understanding and meaning preserva...
arXiv Computation and Language
Aug 31
Synth-JDoc: Synthesizing a Japanese Document Image Dataset for OCR with Diverse Layouts and Embedded Images
The paper introduces Synth-JDoc, a synthetic Japanese document image dataset created by rendering text with HTML and CSS to produce multi‑column layouts that include both vertical and horizontal writing styles. Images generated by text‑to‑image models are embedded to enhance visual realism, and noise and degradation filters are applied to improve robustness. Experiments show that fine‑tuning Large Vision Language Models on Synth‑JDoc yields superior performance on reading vertically written Japanese text compared to prior synthetic datasets.
arXiv AI
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Improving Visual Sensitivity of LLMs on Multimodal Machine Translation with Metric-based Loss Weighting
The paper proposes Metric-based Loss Weighting to enhance visual grounding in multimodal machine translation. By increasing loss for tokens that benefit from image context—identified via the Point-wise Cross-mutual Information (PCXMI) metric and its Congruency-based variant—the method improves translation accuracy on the CoMMuTE dataset by over 7 percentage points. Experiments fine-tune three pretrained multimodal LLMs across three language directions, showing superior performance compared to standard fine-tuning while preserving overall translation quality.
arXiv AI
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VDE Bench: Evaluating The Capability of Image Editing Models to Modify Visual Documents
arXiv:2602. 00122v3 Announce Type: replace-cross Abstract: In recent years, image editing models have made significant progress, enabling users to manipulate visual content in a flexible and interactive manner through natural language instructions.
arXiv Machine Learning
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Multilingual OCR-Aware Fine-Tuning and Prompt-Guided Chain-of-Thought Reasoning for Multimodal Large Language Models
arXiv:2605. 16409v3 Announce Type: replace-cross Abstract: Optical character recognition (OCR) and multilingual scene-text understanding remain challenging for multimodal large language models (MLLMs), particularly in real-world images containing small or degraded text, cluttered layouts, occlusion, handwriting, and complex typography.
arXiv AI
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TextRich: A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2
arXiv:2606. 19259v2 Announce Type: replace-cross Abstract: Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information.