Name
Abstract | ||
|---|---|---|
A correct localisation of tables in a document is instrumental for determining their structure and extracting their contents; therefore, table detection is a key step in table understanding. Nowadays, the most successful methods for table detection in document images employ deep learning algorithms; and, particularly, a technique known as fine-tuning. In this context, such a technique exports the knowledge acquired to detect objects in natural images to detect tables in document images. However, there is only a vague relation between natural and document images, and fine-tuning works better when there is a close relation between the source and target task. In this paper, we show that it is more beneficial to employ fine-tuning from a closer domain. To this aim, we train different object detection algorithms (namely, Mask R-CNN, RetinaNet, SSD and YOLO) using the TableBank dataset (a dataset of images of academic documents designed for table detection and recognition), and fine-tune them for several heterogeneous table detection datasets. Using this approach, we considerably improve the accuracy of the detection models fine-tuned from natural images (in mean a 17%, and, in the best case, up to a 60%). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-57058-3_15 | DAS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
21 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Casado-García Ángela | 1 | 0 | 0.34 |
| César Domínguez | 2 | 95 | 18.93 |
| Jónathan Heras | 3 | 94 | 23.31 |
| Eloy J. Mata | 4 | 11 | 6.38 |
| Vico Pascual | 5 | 58 | 13.19 |