Name
Abstract | ||
|---|---|---|
Table detection is a crucial step in many document analysis applications as tables are used for presenting essential information to readers in a structured manner. It is still a challenging problem due to the variety of table structures and the complexity of document layout. This paper presents a hybrid method consisting of three fundamental steps to detect table zones: classification of the regions, detection of the tables that constitute intersecting horizontal and vertical lines, and identification of the tables made up by only parallel lines. Experiments on the UW-III dataset show that the obtained results are very promising. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3184066.3184091 | 2ND INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND SOFT COMPUTING (ICMLSC 2018) |
Keywords | Field | DocType |
Table detection, document layout analysis, random forest, support vector machine | Data mining,Document analysis,Horizontal and vertical,Pattern recognition,Computer science,Document layout analysis,Document layout,Support vector machine,Parallel,Artificial intelligence,Random forest | Conference |
Citations | PageRank | References |
1 | 0.36 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Van T. Huynh | 1 | 5 | 2.85 |
| Khuong Nguyen-An | 2 | 7 | 2.59 |
| Trinh Le Ba Khanh | 3 | 1 | 0.36 |
| Hyungjeong Yang | 4 | 455 | 47.05 |
| Tuan Anh Tran | 5 | 28 | 3.22 |
| Soo-Hyung Kim | 6 | 191 | 49.03 |