Name
Abstract | ||
|---|---|---|
Table detection is of great significance for the documents analysis and recognition. Although many methods have been proposed and great progress have been made, it is still a great challenge to recognize the less-ruled tables due to the lack of table line features. In this paper, we propose a novel network to generate the layout features for table text to improve the performance of less-ruled table recognition. This feature generator model is similar to the Generative Adversarial Networks (GAN). We force the feature generator model to extract similar features for both ruling tables and less-ruled tables. It can be added into some common object detection and semantic segmentation models such as Mask R-CNN, U-Net. Extensive experiments are conducted on the dataset of ICDAR2017 Page Object Detection Competition dataset and a closed dataset full of the less-ruled tables and non-ruled tables. The primary experimental results show that the proposed GAN-based feature generator is very helpful for less-ruled table detection. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDAR.2019.00127 | ICDAR |
Field | DocType | Citations |
Computer vision,Object detection,Generative adversarial network,Pattern recognition,Computer science,Segmentation,Common object,Artificial intelligence,Generative grammar | Conference | 1 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yibo Li | 1 | 27 | 6.36 |
| Liangcai Gao | 2 | 209 | 30.92 |
| Zhi Tang | 3 | 5 | 1.28 |
| Qinqin Yan | 4 | 1 | 0.36 |
| Yilun Huang | 5 | 5 | 1.78 |