LGDNet: Table Detection Network Combining Local and Global Features

LU Di; YUAN Xuan

doi:10.11999/JEIT240428

Volume 46 Issue 12

Dec. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(12): 4553-4562

LU Di, YUAN Xuan. LGDNet: Table Detection Network Combining Local and Global Features[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4553-4562. doi: 10.11999/JEIT240428

Citation:

LU Di, YUAN Xuan. LGDNet: Table Detection Network Combining Local and Global Features[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4553-4562. doi: 10.11999/JEIT240428

LU Di, YUAN Xuan. LGDNet: Table Detection Network Combining Local and Global Features[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4553-4562. doi: 10.11999/JEIT240428

Citation:

LU Di, YUAN Xuan. LGDNet: Table Detection Network Combining Local and Global Features[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4553-4562. doi: 10.11999/JEIT240428

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LGDNet: Table Detection Network Combining Local and Global Features

doi: 10.11999/JEIT240428 cstr: 32379.14.JEIT240428

LU Di^,,
YUAN Xuan

1.
School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
2.
Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception, Harbin University of Science and Technology, Harbin 150080, China

Received Date: 2024-05-30
Rev Recd Date: 2024-11-08

Available Online: 2024-11-18

Publish Date: 2024-12-01

Abstract

Abstract

In the era of big data, table widely exists in various document images, and table detection is of great significance for the reuse of table information. In response to issues such as limited receptive field, reliance on predefined proposals, and inaccurate table boundary localization in existing table detection algorithms based on convolutional neural network, a table detection network based on DINO model is proposed in this paper. Firstly, an image preprocessing method is designed to enhance the corner and line features of table, enabling more precise table boundary localization and effective differentiation between table and other document elements like text. Secondly, a backbone network SwTNet-50 is designed, and Swin Transformer Blocks (STB) are introduced into ResNet to effectively combine local and global features, and the feature extraction ability of the model and the detection accuracy of table boundary are improved. Finally, to address the inadequacies in encoder feature learning in one-to-one matching and insufficient positive sample training in the DINO model, a collaborative hybrid assignments training strategy is adopted to improve the feature learning ability of the encoder and detection precision. Compared with various table detection methods based on deep learning, our model is better than other algorithms on the TNCR table detection dataset, with F1-Scores of 98.2%, 97.4%, and 93.3% for IoU thresholds of 0.5, 0.75, and 0.9, respectively. On the IIIT-AR-13K dataset, the F1-Score is 98.6% when the IoU threshold is 0.5.
- Table detection,
- Convolutional Neural Network (CNN),
- Transformer,
- Feature extraction

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References(25)

References

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