Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Table detection in online ink notes.

Zhouchen Lin1, Junfeng He, Zhicheng Zhong

  • 1Microsoft Research Asia, 5th Floor, Sigma Building Zhichun Road #49, Haidian District, Beijing 100080, PR China. zhoulin@microsoft.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 5, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A machine learning-based classification model for interstitial lung disease in rheumatoid arthritis.

Frontiers in medicine·2026
Same author

Nodeless superconducting gap and electron-boson coupling in (La,Pr,Sm)<sub>3</sub>Ni<sub>2</sub>O<sub>7</sub> films.

Science (New York, N.Y.)·2026
Same author

miR-206 regulates hypoxia-induced mitophagy and phenotypic remodeling in yak pulmonary artery smooth muscle cells.

Molecular biology of the cell·2026
Same author

Functional analysis of inducible choline/carboxylesterase CCE01 associated with fenpropathrin and abamectin detoxification in Tetranychus urticae (Koch).

Pesticide biochemistry and physiology·2026
Same author

Expression of BDNF-TrkB-AKT1 pathway components and apoptosis-related factors across yak brain regions at low and high altitudes.

Folia histochemica et cytobiologica·2026
Same author

Discovery of Novel, Potent, and Selective IRAK1 Inhibitors as Potential Therapeutics for Hepatocellular Carcinoma.

Journal of medicinal chemistry·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

This study introduces a system for detecting and structuring tables from online ink notes. The technology accurately extracts table data, enabling further editing and analysis of complex or challenging table formats.

Area of Science:

  • Computer Vision
  • Document Analysis
  • Human-Computer Interaction

Background:

  • Tables are crucial for presenting structured information in documents.
  • Extracting and editing tables from free-form digital notes remains a challenge.

Purpose of the Study:

  • To develop a robust system for detecting tables in online ink notes.
  • To extract the structural and logical information of detected tables.
  • To enable multi-way editing of extracted table data.

Main Methods:

  • Primitive table structure detection (ruling lines, bounding boxes) from drawing strokes.
  • Logical structure determination via skeleton normalization and cell content extraction.
  • A decision-tree-based detection process for efficient candidate filtering.

Related Experiment Videos

Main Results:

  • The system demonstrates robustness in identifying tables from free-style ink notes.
  • Accurate extraction of both primitive and logical table structures is achieved.
  • Effectiveness is shown even with complex table layouts and challenging drawing conditions.

Conclusions:

  • The developed system offers an effective solution for table detection and structure extraction from digital ink.
  • The approach facilitates the repurposing and editing of tabular data from unstructured notes.
  • The system's accuracy and robustness make it suitable for real-world applications.