125 citations · 147 across the 9 of their papers we have counts for
11 papers
MonkeyOCRv2: A Visual-Text Foundation Model for Document AI
Yuliang Liu, Zhang Li, Ziyang Zhang +11
Mainstream visual encoders are pretrained on natural images and cannot be effectively applied to document images without document-oriented adaptation, as dense text and fine-graine…
MDPBench: A Benchmark for Multilingual Document Parsing in Real-World Scenarios
Zhang Li, Zhibo Lin, Qiang Liu +7
We introduce Multilingual Document Parsing Benchmark, the first benchmark for multilingual digital and photographed document parsing. Document parsing has made remarkable strides,…
MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns
Jiarui Zhang, Yuliang Liu, Zijun Wu +17
Document parsing is a core task in document intelligence, supporting applications such as information extraction, retrieval-augmented generation, and automated document analysis. H…
LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance
Zhang Li, Biao Yang, Qiang Liu +7
While large multi-modal models (LMMs) demonstrate promising capabilities in segmentation and comprehension, they still struggle with two limitations: inaccurate segmentation and ha…
MSTAR: Box-free Multi-query Scene Text Retrieval with Attention Recycling
Liang Yin, Xudong Xie, Zhang Li +2
Scene text retrieval has made significant progress with the assistance of accurate text localization. However, existing approaches typically require costly bounding box annotations…
MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm
Zhang Li, Yuliang Liu, Qiang Liu +8
We introduce MonkeyOCR, a document parsing model that advances the state of the art by leveraging a Structure-Recognition-Relation (SRR) triplet paradigm. This design simplifies wh…
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…
Exploring the Capabilities of Large Multimodal Models on Dense Text
Shuo Zhang, Biao Yang, Zhang Li +3
While large multi-modal models (LMM) have shown notable progress in multi-modal tasks, their capabilities in tasks involving dense textual content remains to be fully explored. Den…
TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document
Yuliang Liu, Biao Yang, Qiang Liu +4
We present TextMonkey, a large multimodal model (LMM) tailored for text-centric tasks. Our approach introduces enhancement across several dimensions: By adopting Shifted Window Att…
Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models
Zhang Li, Biao Yang, Qiang Liu +6
Large Multimodal Models (LMMs) have shown promise in vision-language tasks but struggle with high-resolution input and detailed scene understanding. Addressing these challenges, we…
OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models
Yuliang Liu, Zhang Li, Mingxin Huang +7
Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks…