Graph Convolutional Networks for Unified Document Line and Paragraph Detection
By
colonCapitalDee
A respectable bake. You'd come back tomorrow for another.
Summary
This research paper presents a unified approach for detecting lines and paragraphs in documents using graph convolutional networks. The method formulates the task as a two-level clustering problem where text detection boxes (words) are clustered into lines, and lines are clustered into paragraphs, forming a hierarchical tree structure representing document layout. The approach demonstrates high efficiency while achieving state-of-the-art quality for paragraph detection in both public benchmarks and real-world images.
Key quotes
· 4 pulledWe formulate the task of detecting lines and paragraphs in a document into a unified two-level clustering problem
Given a set of text detection boxes that roughly correspond to words, a text line is a cluster of boxes and a paragraph is a cluster of lines
These clusters form a two-level tree that represents a major part of the layout of a document
We use a graph convolutional network to predict the relations between text detection boxes and then build both levels of clusters from these predictions
You might also wanna read
Apple to present 14 AI research papers at CVPR conference in Denver ahead of WWDC
Apple will present 14 AI research papers at the 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) in Denver next we
appleinsider.com·3d ago
OpenAI's o3 model shows surprising geoguessing capabilities from photos
The article discusses how OpenAI's o3 model unexpectedly demonstrated impressive geoguessing capabilities, able to identify locations from n
seangoedecke.com·10d ago
LoGeR: Hybrid Memory System Enables Dense 3D Reconstruction from Long Videos
LoGeR (Long-Context Geometric Reconstruction with Hybrid Memory) is a novel AI system developed by Google DeepMind and UC Berkeley researche
ShapeR: Robust Conditional 3D Shape Generation from Casual Image Captures
ShapeR is a novel system for generative, object-centric 3D reconstruction from casual image sequences. The research paper presents a method
Apple's SHARP: Photorealistic 3D View Synthesis from a Single Image in Under a Second
Apple researchers present SHARP, a neural network approach for photorealistic view synthesis from a single image. The method regresses param
github.com·5mo ago
ALIGN-Parts: One-Shot 3D Part Segmentation and Naming via Set-Level Alignment
The article discusses ALIGN-Parts, a new approach for 3D part segmentation and naming that addresses the challenge of inconsistent labeling
name-that-part.github.io·5mo ago