Papers Document Layout Analysis
“Document Layout Analysis” 태그가 달린 논문 121편 · 필터 해제
BaFCo: A Document Understanding Benchmark for Complex Bangla Form Comprehension
Document comprehension is a challenging yet impactful task for Multimodal Large Language Models, especially as these systems see growing adoption in real-world, human-centric applications. However, this adoption is limit…
Key Information ExtractionDocument Layout AnalysisRT-DocLayout: Real-Time End-to-End Document Layout Analysis with Reading Order in the Wild
Accurate document layout analysis remains a critical bottleneck for document parsing systems, due to the intricate coupling among heterogeneous document layout elements, geometric distortions (\eg, paper warping and bend…
Document Layout AnalysisBounding Box Label Propagation for Re-Annotation of Document Layout Analysis Datasets
Datasets in practical document processing scenarios typically grow over time, and their class annotations undergo continuous refinement. This creates significant re-annotation efforts, which are time-consuming and costly…
Document Layout AnalysisObject DetectionBenchmarking Open-Source Layout Detection Models for Data Snapshot Extraction from Institutional Documents
Institutional documents contain substantial amounts of operational and analytical information embedded within figures and tables. Current approaches for extracting visual content from documents are largely built around g…
Document Layout AnalysisHow Do Document Parsers Break? Auditing Structural Vulnerability in Document Intelligence
Document Layout Analysis (DLA) pipelines provide structured page representations for retrieval-augmented generation, long-document question answering, and other document intelligence systems, yet their robustness evaluat…
Document Layout AnalysisQuestion AnsweringParser-Oriented Structural Refinement for a Stable Layout Interface in Document Parsing
Accurate document parsing requires both robust content recognition and a stable parser interface. In explicit Document Layout Analysis (DLA) pipelines, downstream parsers do not consume the full detector output. Instead,…
Document Layout AnalysisLED: A Benchmark for Evaluating Layout Error Detection in Document Analysis
Recent advances in Large Language Models (LLMs) and Large Multimodal Models (LMMs) have improved Document Layout Analysis (DLA), yet structural errors such as region merging, splitting, and omission remain persistent. Co…
Document Layout AnalysisThe COTe score: A decomposable framework for evaluating Document Layout Analysis models
Document Layout Analysis (DLA) is the process by which a page is parsed into meaningful elements, often using machine learning models. Typically, the quality of a model is judged using general machine vision metrics such…
Document Layout AnalysisObject DetectionPromptDLA: A Domain-aware Prompt Document Layout Analysis Framework with Descriptive Knowledge as a Cue
Document Layout Analysis (DLA) is crucial for document artificial intelligence and has recently received increasing attention, resulting in an influx of large-scale public DLA datasets. Existing work often combines data …
Document Layout AnalysisTowards Khmer Scene Document Layout Detection
While document layout analysis for Latin scripts has advanced significantly, driven by the advent of large multimodal models (LMMs), progress for the Khmer language remains constrained because of the scarcity of annotate…
Document Layout AnalysisDocDjinn: Controllable Synthetic Document Generation with VLMs and Handwriting Diffusion
Effective document intelligence models rely on large amounts of annotated training data. However, procuring sufficient and high-quality data poses significant challenges due to the labor-intensive and costly nature of da…
Key Information ExtractionDocument Layout AnalysisDocument ClassificationQuestion AnsweringPARL: Position-Aware Relation Learning Network for Document Layout Analysis
Document layout analysis aims to detect and categorize structural elements (e.g., titles, tables, figures) in scanned or digital documents. Popular methods often rely on high-quality Optical Character Recognition (OCR) t…
Document Layout AnalysisIndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing
Document layout analysis is essential for downstream tasks such as information retrieval, extraction, OCR, and digitization. However, existing large-scale datasets like PubLayNet and DocBank lack fine-grained region labe…
Document Layout AnalysisInformation RetrievalLayout-Aware Text Editing for Efficient Transformation of Academic PDFs to Markdown
Academic documents stored in PDF format can be transformed into plain text structured markup languages to enhance accessibility and enable scalable digital library workflows. Markup languages allow for easier updates and…
Document Layout AnalysisKH-FUNSD: A Hierarchical and Fine-Grained Layout Analysis Dataset for Low-Resource Khmer Business Document
Automated document layout analysis remains a major challenge for low-resource, non-Latin scripts. Khmer is a language spoken daily by over 17 million people in Cambodia, receiving little attention in the development of d…
Document Layout AnalysisInformation ExtractionDocument AIHybriDLA: Hybrid Generation for Document Layout Analysis
Conventional document layout analysis (DLA) traditionally depends on empirical priors or a fixed set of learnable queries executed in a single forward pass. While sufficient for early-generation documents with a small, p…
Document Layout AnalysisLLM-Guided Probabilistic Fusion for Label-Efficient Document Layout Analysis
Document layout understanding remains data-intensive despite advances in semi-supervised learning. We present a framework that enhances semi-supervised detection by fusing visual predictions with structural priors from t…
Document Layout AnalysisOmniDocLayout: Towards Diverse Document Layout Generation via Coarse-to-Fine LLM Learning
Document AI has advanced rapidly and is attracting increasing attention. Yet, while most efforts have focused on document layout analysis (DLA), its generative counterpart, layout generation, remains underexplored. Disti…
Document Layout AnalysisDocument AIImproving OCR for Historical Texts of Multiple Languages
This paper presents our methodology and findings from three tasks across Optical Character Recognition (OCR) and Document Layout Analysis using advanced deep learning techniques. First, for the historical Hebrew fragment…
Document Layout AnalysisHandwriting RecognitionSemantic SegmentationData AugmentationHySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis
We present HySemRAG, a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG) to automate large-scale literature synthesis and identify methodological research gaps. Th…
Document Layout AnalysisSemantic SimilaritySemantic Retrieval