Table Structure Detection Based on PPLCNet Network

This paper presents table structure recognition models based on PPLCNet, MobileNetV3, and ResNet, evaluated on the PubTabNet dataset. By incorporating residual structures and the CBAM attention mechanism into PPLCNet, we achieve an accuracy of 73.53% and a TEDS of 94.57%. Ablation and comparative experiments validate the effectiveness of these enhancements.

Authors

Lei Wu (ORCID: https://orcid.org/0000-0002-0818-7338)
Yadi Tan (ORCID: https://orcid.org/0000-0002-5009-1057)
Shunli Chen (ORCID: https://orcid.org/0009-0004-7056-4853)
Qiang Wang (ORCID: https://orcid.org/0000-0002-7366-5674)
Yu Wu
Chenyu Wang (ORCID: https://orcid.org/0000-0002-3675-3616)

Institutions

Zhejiang University of Science and Technology (CN)
Runze (China) (CN)
Hangzhou Wanxiang Polytechnic (CN)
Zhejiang University (CN)

Publication Details

Published: 2024-12-20
DOI: https://doi.org/10.1109/pic62406.2024.10892687
Primary Topic: Advanced Computational Techniques and Applications
Type: article
Field-Weighted Citation Impact: 0.00

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article

Table Structure Detection Based on PPLCNet Network

Lei Wu, Yadi Tan, Shunli Chen, Qiang Wang et al.

Dec 20, 2024

Advanced Computational Techniques and Applications

article

Table Structure Detection Based on PPLCNet Network

Lei Wu, Yadi Tan, Shunli Chen, Qiang Wang, Yu Wu, Chenyu Wang

December 20, 2024article en

Abstract

This paper presents table structure recognition models based on PPLCNet, MobileNetV3, and ResNet, evaluated on the PubTabNet dataset. By incorporating residual structures and the CBAM attention mechanism into PPLCNet, we achieve an accuracy of 73.53% and a TEDS of 94.57%. Ablation and comparative experiments validate the effectiveness of these enhancements.

Zhejiang University of Science and Technology (CN), Runze (China) (CN), Hangzhou Wanxiang Polytechnic (CN), Zhejiang University (CN)

Openalex Percentile: Top 75%

Advanced Computational Techniques and Applications

DOI Source OpenAlex

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