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DCTable: A Dilated CNN with Optimizing Anchors for Accurate Table Detection.

Takwa Kazdar¹, Wided Souidene Mseddi¹, Moulay A Akhloufi²

¹Sercom Laboratory, Ecole Polytechnique de Tunisie, Université de Carthage, La Marsa 2078, Tunisia.

Journal of Imaging

|March 28, 2023

Summary

This summary is machine-generated.

DCTable enhances table detection by improving feature extraction and optimizing anchors with IoU-balanced loss. This novel method boosts accuracy and reduces false positives in deep learning-based table recognition systems.

Keywords:

Faster R-CNN anchors bilinear interpolation dilated convolutions table detection

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep learning is the mainstream in table detection.
Challenges exist in detecting tables with complex layouts or small sizes.

Purpose of the Study:

To propose DCTable, a novel method to improve Faster R-CNN for enhanced table detection.
To address limitations in detecting challenging tables.

Main Methods:

Utilized dilated convolutions in the backbone for more discriminative feature extraction.
Optimized anchors using Intersection over Union (IoU)-balanced loss for the Region Proposal Network (RPN).
Implemented RoI Align layer with bilinear interpolation for accurate mapping of region proposal candidates.

Main Results:

Demonstrated effectiveness of DCTable on public datasets (ICDAR 2017-Pod, ICDAR-2019, Marmot, RVL CDIP).
Achieved considerable improvement in F1-score compared to existing methods.
Reduced false positive rates through anchor optimization.

Conclusions:

DCTable effectively improves table detection accuracy and robustness.
The proposed method offers a significant advancement in deep learning-based table detection.