29 citations · 58 across the 12 of their papers we have counts for
12 papers
From Diagnosis to Correction: Benchmarking and Improving Real-World Table Parsing
Jutao Xiao, Yuan Qu, Dongsheng Ma +7
Recent document parsers achieve table TEDS scores above 93 on OmniDocBench v1.6, yet community feedback and our audit reveal persistent failures on complex real-world tables. To qu…
MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale
Bin Wang, Tianyao He, Linke Ouyang +40
Current document parsing methods advance primarily through model architecture innovation, while systematic engineering of training data remains underexplored. Yet state-of-the-art…
CPT: Controllable and Editable Design Variations with Language Models
Karthik Suresh, Amine Ben Khalifa, Li Zhang +4
Designing visually diverse and high-quality designs remains a manual, time-consuming process, limiting scalability and personalization in creative workflows. We present a system fo…
MoDora: Tree-Based Semi-Structured Document Analysis System
Bangrui Xu, Qihang Yao, Zirui Tang +8
Semi-structured documents integrate diverse interleaved data elements (e.g., tables, charts, hierarchical paragraphs) arranged in various and often irregular layouts. These documen…
TRivia: Self-supervised Fine-tuning of Vision-Language Models for Table Recognition
Junyuan Zhang, Bin Wang, Qintong Zhang +13
Table recognition (TR) aims to transform table images into semi-structured representations such as HTML or Markdown. As a core component of document parsing, TR has long relied on…
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
Junbo Niu, Zheng Liu, Zhuangcheng Gu +58
We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational effi…
OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations
Linke Ouyang, Yuan Qu, Hongbin Zhou +17
Document content extraction is a critical task in computer vision, underpinning the data needs of large language models (LLMs) and retrieval-augmented generation (RAG) systems. Des…
MinerU: An Open-Source Solution for Precise Document Content Extraction
Bin Wang, Chao Xu, Xiaomeng Zhao +15
Document content analysis has been a crucial research area in computer vision. Despite significant advancements in methods such as OCR, layout detection, and formula recognition, e…
Image Over Text: Transforming Formula Recognition Evaluation with Character Detection Matching
Bin Wang, Fan Wu, Linke Ouyang +5
Formula recognition presents significant challenges due to the complicated structure and varied notation of mathematical expressions. Despite continuous advancements in formula rec…
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI Data
Bin Wang, Linke Ouyang, Fan Wu +7
In the era of artificial intelligence, the diversity of data modalities and annotation formats often renders data unusable directly, requiring understanding and format conversion b…
InternLM2 Technical Report
Zheng Cai, Maosong Cao, Haojiong Chen +97
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advan…
VIGC: Visual Instruction Generation and Correction
Bin Wang, Fan Wu, Xiao Han +8
The integration of visual encoders and large language models (LLMs) has driven recent progress in multimodal large language models (MLLMs). However, the scarcity of high-quality in…