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Document Layout Analysis
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"Document Layout Analysis is performed to determine physical structure of a document, that is, to determine document components. These document components can consist of single connected components-regions [...] of pixels that are adjacent to form single regions [...] , or group of text lines. A text line is a group of characters, symbols, and words that are adjacent, “relatively close” to each other and through which a straight line can be drawn (usually with horizontal or vertical orientation)." L. O'Gorman, "The document spectrum for page layout analysis," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1162-1173, Nov. 1993.
Image credit: PubLayNet: largest dataset ever for document layout analysis
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
From Codicology to Code: A Comparative Study of Transformer and YOLO-based Detectors for Layout Analysis in Historical Documents
Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation
MRI Image Generation Based on Text Prompts
SCAN: Semantic Document Layout Analysis for Textual and Visual Retrieval-Augmented Generation
A document processing pipeline for the construction of a dataset for topic modeling based on the judgments of the Italian Supreme Court
Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs
How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings
AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization
SFDLA: Source-Free Document Layout Analysis
PP-DocLayout: A Unified Document Layout Detection Model to Accelerate Large-Scale Data Construction
UniHDSA: A Unified Relation Prediction Approach for Hierarchical Document Structure Analysis
Do image and video quality metrics model low-level human vision?
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
Pretext Task Adversarial Learning for Unpaired Low-field to Ultra High-field MRI Synthesis
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization
EDocNet: Efficient Datasheet Layout Analysis Based on Focus and Global Knowledge Distillation
FD-LSCIC: Frequency Decomposition-based Learned Screen Content Image Compression
Interleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation
Graph-based Document Structure Analysis
DoPTA: Improving Document Layout Analysis using Patch-Text Alignment
DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression
Good, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion
MCUCoder: Adaptive Bitrate Learned Video Compression for IoT Devices
DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception
USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s
PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction
Visual Verity in AI-Generated Imagery: Computational Metrics and Human-Centric Analysis
MicroSSIM: Improved Structural Similarity for Comparing Microscopy Data
Bidirectional Stereo Image Compression with Cross-Dimensional Entropy Model
DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents
A Study on the Effect of Color Spaces in Learned Image Compression
Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications
UnSupDLA: Towards Unsupervised Document Layout Analysis
Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises
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