Papers document understanding
“document understanding” 태그가 달린 논문 309편 · 필터 해제
A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends
Visually-Rich Document Understanding (VRDU) has emerged as a critical field, driven by the need to automatically process documents containing complex visual, textual, and layout information. Recently, Multimodal Large La…
document understandingOptical Character RecognitionOptical Character Recognition (OCR)PaddleOCR 3.0 Technical Report
This technical report introduces PaddleOCR 3.0, an Apache-licensed open-source toolkit for OCR and document parsing. To address the growing demand for document understanding in the era of large language models, PaddleOCR…
document understandingKey Information ExtractionOptical Character Recognition (OCR)GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
We present GLM-4.1V-Thinking, a vision-language model (VLM) designed to advance general-purpose multimodal understanding and reasoning. In this report, we share our key findings in the development of the reasoning-centri…
document understandingMultimodal ReasoningVideo UnderstandingClass-Agnostic Region-of-Interest Matching in Document Images
Document understanding and analysis have received a lot of attention due to their widespread application. However, existing document analysis solutions, such as document layout analysis and key information extraction, ar…
Document Layout Analysisdocument understandingKey Information ExtractionDrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images
Visual grounding in text-rich document images is a critical yet underexplored challenge for document intelligence and visual question answering (VQA) systems. We present \drishtikon, a multi-granular visual grounding fra…
document understandingOptical Character Recognition (OCR)Question AnsweringVisual Grounding+2Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models
Recent advancements in multimodal large language models have enhanced document understanding by integrating textual and visual information. However, existing models exhibit incompleteness within their paradigm in real-wo…
document understandingHallucinationOptical Character Recognition (OCR)PP-DocBee2: Improved Baselines with Efficient Data for Multimodal Document Understanding
This report introduces PP-DocBee2, an advanced version of the PP-DocBee, designed to enhance multimodal document understanding. Built on a large multimodal model architecture, PP-DocBee2 addresses the limitations of its …
document understandingWikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts
Documents are fundamental to preserving and disseminating information, often incorporating complex layouts, tables, and charts that pose significant challenges for automatic document understanding (DU). While vision-lang…
document understandingMultiple-choiceQuestion AnsweringSimpleDoc: Multi-Modal Document Understanding with Dual-Cue Page Retrieval and Iterative Refinement
Document Visual Question Answering (DocVQA) is a practical yet challenging task, which is to ask questions based on documents while referring to multiple pages and different modalities of information, e.g, images and tab…
document understandingQuestion AnsweringRAGRe-Ranking+3A Survey on Vietnamese Document Analysis and Recognition: Challenges and Future Directions
Vietnamese document analysis and recognition (DAR) is a crucial field with applications in digitization, information retrieval, and automation. Despite advancements in OCR and NLP, Vietnamese text recognition faces uniqu…
Computational Efficiencydocument understandingDomain AdaptationInformation Retrieval+2DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning
Zero-shot Event Detection (ED), the task of identifying event mentions in natural language text without any training data, is critical for document understanding in specialized domains. Understanding the complex event on…
document understandingEvent DetectionTransfer LearningLEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World
This paper presents LEMONADE, a large-scale conflict event dataset comprising 39,786 events across 20 languages and 171 countries, with extensive coverage of region-specific entities. LEMONADE is based on a partially rea…
document understandingEntity LinkingEvent ExtractionZero-shot Event ExtractionInfinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing
Automated parsing of scanned documents into richly structured, machine-readable formats remains a critical bottleneck in Document AI, as traditional multi-stage pipelines suffer from error propagation and limited adaptab…
Document AIdocument understandingLanguage ModelingLanguage Modelling+4Point-RFT: Improving Multimodal Reasoning with Visually Grounded Reinforcement Finetuning
Recent advances in large language models have significantly improved textual reasoning through the effective use of Chain-of-Thought (CoT) and reinforcement learning. However, extending these successes to vision-language…
document understandingMultimodal ReasoningVisual ReasoningMT$^{3}$: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning
Text Image Machine Translation (TIMT)-the task of translating textual content embedded in images-is critical for applications in accessibility, cross-lingual information access, and real-world document understanding. How…
document understandingMachine TranslationOptical Character RecognitionOptical Character Recognition (OCR)+2Doc-CoB: Enhancing Multi-Modal Document Understanding with Visual Chain-of-Boxes Reasoning
Multimodal large language models (MLLMs) have made significant progress in document understanding. However, the information-dense nature of document images still poses challenges, as most queries depend on only a few rel…
document understandingVisual ReasoningARB: A Comprehensive Arabic Multimodal Reasoning Benchmark
As Large Multimodal Models (LMMs) become more capable, there is growing interest in evaluating their reasoning processes alongside their final outputs. However, most benchmarks remain focused on English, overlooking lang…
document understandingMultimodal ReasoningOptical Character Recognition (OCR)Visual ReasoningThe Hidden Structure -- Improving Legal Document Understanding Through Explicit Text Formatting
Legal contracts possess an inherent, semantically vital structure (e.g., sections, clauses) that is crucial for human comprehension but whose impact on LLM processing remains under-explored. This paper investigates the e…
document understandingOptical Character Recognition (OCR)Prompt EngineeringQuestion AnsweringWildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild?
The rapid advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced capabilities in Document Understanding. However, prevailing benchmarks like DocVQA and ChartQA predominantly comprise \textit…
document understandingDocument Image Rectification Bases on Self-Adaptive Multitask Fusion
Deformed document image rectification is essential for real-world document understanding tasks, such as layout analysis and text recognition. However, current multi-task methods -- such as background removal, 3D coordina…
document understanding