2 citations · 5 across the 11 of their papers we have counts for
13 papers
DocPO: Advancing Document Policy Optimization via Tailored Step-Aware Rewards
Yunhao Wang, Binghong Wu, Zhenyu Huang +4
Reinforcement learning (RL) for document parsing often relies on reference-based rewards rooted in edit distance (e.g., tree edit distance), yet it remains hard to optimize in the…
HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better
Gengluo Li, Xingyu Wan, Shangpin Peng +20
We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, information extraction, text-image tr…
ChartArena: Benchmarking Chart Parsing across Languages, Scenarios, and Formats
Shangpin Peng, Gengluo Li, Xingyu Wan +10
Charts are a primary medium for conveying quantitative and relational information, yet systematically evaluating chart parsing models remains difficult. Existing benchmarks focus o…
MORE: A Multilingual Document Parsing Benchmark and Evaluation
Long Xu, Binghong Wu, Tinghao Yu +6
Multilingual documents encapsulate rich regional cultures, scientific discoveries, and historical records. Parsing this content into structured, machine-readable formats is critica…
StrucTab: A Structured Optimization Framework for Table Parsing
Gengluo Li, Shangpin Peng, Chengquan Zhang +10
Table parsing aims to convert table images into structured, machine-readable representations, a task requiring the joint perception of complex spatial layouts and textual content.…
Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild
Mao Zheng, Zheng Li, Tao Chen +10
Hy-MT2 is a family of fast-thinking multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B, and 30B-A3B (MoE), all of wh…
Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting
Hao Feng, Shu Wei, Xiang Fei +10
Document image parsing is challenging due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables. Current approaches either assemble specializ…
OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning
Ling Fu, Zhebin Kuang, Jiajun Song +21
Scoring the Optical Character Recognition (OCR) capabilities of Large Multimodal Models (LMMs) has witnessed growing interest. Existing benchmarks have highlighted the impressive p…
Harmonizing Visual Text Comprehension and Generation
Zhen Zhao, Jingqun Tang, Binghong Wu +7
In this work, we present TextHarmony, a unified and versatile multimodal generative model proficient in comprehending and generating visual text. Simultaneously generating images a…
A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding
Jinghui Lu, Haiyang Yu, Yanjie Wang +9
Recently, many studies have demonstrated that exclusively incorporating OCR-derived text and spatial layouts with large language models (LLMs) can be highly effective for document…
TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy
Weichao Zhao, Hao Feng, Qi Liu +9
Tables contain factual and quantitative data accompanied by various structures and contents that pose challenges for machine comprehension. Previous methods generally design task-s…
TextSquare: Scaling up Text-Centric Visual Instruction Tuning
Jingqun Tang, Chunhui Lin, Zhen Zhao +15
Text-centric visual question answering (VQA) has made great strides with the development of Multimodal Large Language Models (MLLMs), yet open-source models still fall short of lea…
Multi-modal In-Context Learning Makes an Ego-evolving Scene Text Recognizer
Zhen Zhao, Jingqun Tang, Chunhui Lin +6
Scene text recognition (STR) in the wild frequently encounters challenges when coping with domain variations, font diversity, shape deformations, etc. A straightforward solution is…