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Graph Neural Networks and Representation Embedding for Table Extraction in PD...
Table Extraction (TE) is redefined as a node classification task, addressed by a GNN. Graph nodes are composed of basic PDF objects while edges are computed considering... -
Towards End-to-End Unified Scene Text Detection and Layout Analysis
Scene text detection and document layout analysis have long been treated as two separate tasks in different image domains. In this paper, we bring them together and introduce... -
PublayNet: largest dataset ever for document layout analysis
The PublayNet dataset is the largest dataset ever for document layout analysis task. -
VTLayout: Fusion of Visual and Text Features for Document Layout Analysis
A document layout analysis model, VTLayout, based on the fusion of deep visual, shallow visual, and text features, is proposed to solve the low recognition rate of different... -
Badlad: A large multi-domain Bengali document layout analysis dataset
Badlad: A large multi-domain Bengali document layout analysis dataset -
BaDLAD: A Comprehensive MViTv2 Based Approach
BaDLAD: A large multi-domain Bengali document layout analysis dataset -
Document Layout Analysis via Dynamic Residual Feature Fusion
Document layout analysis (DLA) aims to split the document image into different interest regions and understand the role of each region. -
Comp-HRDoc
Document layout analysis (DLA) is crucial for understanding the physical layout and logical structure of documents, serving information retrieval, document summarization,... -
DLAFormer: An End-to-End Transformer For Document Layout Analysis
Document layout analysis (DLA) is crucial for understanding the physical layout and logical structure of documents, serving information retrieval, document summarization,... -
RoDLA: Benchmarking the Robustness of Document Layout Analysis Models
A comprehensive robustness benchmark for Document Layout Analysis (DLA) models, including 450K document images from 3 datasets.