19 citations · 87 across the 26 of their papers we have counts for
20 papers · 1 filter
MinerU.Chem: A High-Precision System for Optical Chemical Structure and Reaction Recognition
Haote Yang, Jiang Wu, Jingchao Wang +42
In organic chemistry papers and patents, molecular structures, reaction schemes, and experimental conditions are often presented as molecular structure depictions, reaction diagram…
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…
MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing
Bangrui Xu, Ziyang Miao, Xuanhe Zhou +7
VLM-based OCR models have become the de facto choice for document parsing, as they can accurately extract page-level elements (e.g., paragraphs within individual pages) together wi…
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…
MinerU-Diffusion: Rethinking Document OCR as Inverse Rendering via Diffusion Decoding
Hejun Dong, Junbo Niu, Bin Wang +3
Optical character recognition (OCR) has evolved from line-level transcription to structured document parsing, requiring models to recover long-form sequences containing layout, tab…
Molecular Identifier Visual Prompt and Verifiable Reinforcement Learning for Chemical Reaction Diagram Parsing
Jiahe Song, Chuang Wang, Yinfan Wang +11
Reaction diagram parsing (RxnDP) is critical for extracting chemical synthesis information from literature. Although recent Vision-Language Models (VLMs) have emerged as a promisin…
Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning
Junyuan Gao, Jiahe Song, Jiang Wu +9
Evaluating the multilingual capabilities of Large Vision-Language Models (LVLMs) remains challenging because most benchmarks rely on non-parallel corpora, making it unclear whether…
OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation
Junyuan Zhang, Qintong Zhang, Bin Wang +6
Retrieval-augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge to reduce hallucinations and incorporate up-to-date information without…
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…
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
Qingyun Li, Zhe Chen, Weiyun Wang +37
Image-text interleaved data, consisting of multiple images and texts arranged in a natural document format, aligns with the presentation paradigm of internet data and closely resem…
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
Pan Zhang, Xiaoyi Dong, Yuhang Zang +24
We present InternLM-XComposer-2.5 (IXC-2.5), a versatile large-vision language model that supports long-contextual input and output. IXC-2.5 excels in various text-image comprehens…
DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models
Renqiu Xia, Song Mao, Xiangchao Yan +19
Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data. Leveraging multi-modality data extracted from these docum…
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Zhe Chen, Weiyun Wang, Hao Tian +32
In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models…
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
Xiaoyi Dong, Pan Zhang, Yuhang Zang +21
The Large Vision-Language Model (LVLM) field has seen significant advancements, yet its progression has been hindered by challenges in comprehending fine-grained visual content due…
UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition
Bin Wang, Zhuangcheng Gu, Guang Liang +4
The paper introduces the UniMER dataset, marking the first study on Mathematical Expression Recognition (MER) targeting complex real-world scenarios. The UniMER dataset includes a…
InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model
Xiaoyi Dong, Pan Zhang, Yuhang Zang +20
We introduce InternLM-XComposer2, a cutting-edge vision-language model excelling in free-form text-image composition and comprehension. This model goes beyond conventional vision-l…
OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation
Qidong Huang, Xiaoyi Dong, Pan Zhang +6
Hallucination, posed as a pervasive challenge of multi-modal large language models (MLLMs), has significantly impeded their real-world usage that demands precise judgment. Existing…
Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization
Zhiyuan Zhao, Bin Wang, Linke Ouyang +3
Multimodal large language models have made significant advancements in recent years, yet they still suffer from a common issue known as the "hallucination problem", in which the mo…
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…