TableBorderNet: A Table Border Extraction Network Considering Topological Regularity
Accurate extraction of table borders in scanned road engineering drawings is crucial for the digital transformation of engineering archives, which is an essential step in the development of intelligent infrastructure systems. However, challenges such as degraded borders, image blur, and character ad...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/3899 |
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| author | Jing Yang Shengqiang Zhou Xialing Li Yuchun Huang Honglin Jiang |
| author_facet | Jing Yang Shengqiang Zhou Xialing Li Yuchun Huang Honglin Jiang |
| author_sort | Jing Yang |
| collection | DOAJ |
| description | Accurate extraction of table borders in scanned road engineering drawings is crucial for the digital transformation of engineering archives, which is an essential step in the development of intelligent infrastructure systems. However, challenges such as degraded borders, image blur, and character adjoining often hinder the precise delineation of table structures, making automated parsing difficult. Existing solutions, including traditional OCR tools and deep learning methods, struggle to consistently delineate table borders in the presence of these visual distortions and fail to perform well without extensive annotated datasets, which limits their effectiveness in real-world applications. We propose TableBorderNet, a semantic segmentation framework designed for precise border extraction under complex visual conditions. The framework captures structural context by guiding convolutional feature extraction along explicit row and column directions, enabling more accurate delineation of table borders. To ensure topological consistency in complex or degraded inputs, a topology-aware loss function is introduced, which explicitly penalizes structural discontinuities during training. Additionally, a generative self-supervised strategy simulates common degradation patterns, allowing the model to achieve strong performance with minimal reliance on manually annotated data. Experiments demonstrate that the method achieves an Intersection-over-Union of 94.2% and a topological error of 1.07%, outperforming existing approaches. These results underscore its practicality and scalability for accelerating the digitization of engineering drawings in support of data-driven road asset management. |
| format | Article |
| id | doaj-art-5ef09c605b2a44b9ab89fded52f7891a |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5ef09c605b2a44b9ab89fded52f7891a2025-08-20T02:36:31ZengMDPI AGSensors1424-82202025-06-012513389910.3390/s25133899TableBorderNet: A Table Border Extraction Network Considering Topological RegularityJing Yang0Shengqiang Zhou1Xialing Li2Yuchun Huang3Honglin Jiang4School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaChina Railway Changjiang Transport Design Group Co., Ltd., Chongqing 401121, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaChina Railway Changjiang Transport Design Group Co., Ltd., Chongqing 401121, ChinaAccurate extraction of table borders in scanned road engineering drawings is crucial for the digital transformation of engineering archives, which is an essential step in the development of intelligent infrastructure systems. However, challenges such as degraded borders, image blur, and character adjoining often hinder the precise delineation of table structures, making automated parsing difficult. Existing solutions, including traditional OCR tools and deep learning methods, struggle to consistently delineate table borders in the presence of these visual distortions and fail to perform well without extensive annotated datasets, which limits their effectiveness in real-world applications. We propose TableBorderNet, a semantic segmentation framework designed for precise border extraction under complex visual conditions. The framework captures structural context by guiding convolutional feature extraction along explicit row and column directions, enabling more accurate delineation of table borders. To ensure topological consistency in complex or degraded inputs, a topology-aware loss function is introduced, which explicitly penalizes structural discontinuities during training. Additionally, a generative self-supervised strategy simulates common degradation patterns, allowing the model to achieve strong performance with minimal reliance on manually annotated data. Experiments demonstrate that the method achieves an Intersection-over-Union of 94.2% and a topological error of 1.07%, outperforming existing approaches. These results underscore its practicality and scalability for accelerating the digitization of engineering drawings in support of data-driven road asset management.https://www.mdpi.com/1424-8220/25/13/3899table border extractiondeep learningsemantic segmentationtopology-aware learning |
| spellingShingle | Jing Yang Shengqiang Zhou Xialing Li Yuchun Huang Honglin Jiang TableBorderNet: A Table Border Extraction Network Considering Topological Regularity Sensors table border extraction deep learning semantic segmentation topology-aware learning |
| title | TableBorderNet: A Table Border Extraction Network Considering Topological Regularity |
| title_full | TableBorderNet: A Table Border Extraction Network Considering Topological Regularity |
| title_fullStr | TableBorderNet: A Table Border Extraction Network Considering Topological Regularity |
| title_full_unstemmed | TableBorderNet: A Table Border Extraction Network Considering Topological Regularity |
| title_short | TableBorderNet: A Table Border Extraction Network Considering Topological Regularity |
| title_sort | tablebordernet a table border extraction network considering topological regularity |
| topic | table border extraction deep learning semantic segmentation topology-aware learning |
| url | https://www.mdpi.com/1424-8220/25/13/3899 |
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