Table Detection Method Based on Faster-RCNN and Window Attention
Authors: 
Han Chen, Shengli Song, Rijian SuAuthors Info & Claims
Pages 267 - 273
Abstract
As an important carrier of information, tables possess the characteristics of high data storage density, conciseness, and intuitiveness, and are widely applied in offices and daily life. Due to the complexity of table structures and diverse presentation formats, the automated processing of a large number of image-based tables has always been a challenge in the field of document recognition. This algorithm addresses the task of table detection in table processing and proposes a table detection algorithm based on an improved window self-attention network for feature extraction of image-based tables. It utilizes a two-stage object detection algorithm, introduces local feature extraction blocks and backward feed-forward residual network blocks, and designs a feature pyramid network within the backbone to enhance the model's detection performance by improving its ability to learn document spatial layout features. The effectiveness of the proposed method is verified through experimental comparisons on publicly available datasets.
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Index Terms
- Table Detection Method Based on Faster-RCNN and Window Attention
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Published In
December 2023
310 pages
ISBN:9798400709265
DOI:10.1145/3638837
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 March 2024
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ICNCC 2023
ICNCC 2023: 2023 the 12th International Conference on Networks, Communication and Computing
December 15 - 17, 2023
Osaka, Japan
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