A Clustering Approach Combining Lines and Text Detection for Table Extraction
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-41501-2_10
Reference5 articles.
1. Arif, S., Shafait, F.: Table detection in document images using foreground and background features. In: 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8 (2018). https://doi.org/10.1109/DICTA.2018.8615795
2. Mikhailov, A., Shigarov, A., Rozhkov, E., Cherepanov, I.: On graph-based verification for PDF table detection. In: 2020 Ivannikov Ispras Open Conference (ISPRAS), pp. 91–95 (2020). https://doi.org/10.1109/ISPRAS51486.2020.00020
3. Nidhi, Saluja, K., Mahajan, A., Jadhav, A., Aggarwal, N., Chaurasia, D., Ghosh, D.: Table detection and extraction using opencv and novel optimization methods. In: 2021 International Conference on Computational Performance Evaluation (ComPE), pp. 755–760 (2021). https://doi.org/10.1109/ComPE53109.2021.9752204
4. Ranka, V., Patil, S., Patni, S., Raut, T., Mehrotra, K., Gupta, M.K.: Automatic table detection and retention from scanned document images via analysis of structural information. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1–6 (2017). https://doi.org/10.1109/ICIIP.2017.8313719
5. Riba, P., Dutta, A., Goldmann, L., Fornés, A., Ramos, O., Lladós, J.: Table detection in invoice documents by graph neural networks. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 122–127 (2019). https://doi.org/10.1109/ICDAR.2019.00028
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