Abstract
Document layout analysis (DLA) is an essential step for Optical Character Recognition Systems (OCR). The text of the document fed to the OCR must be extracted first and isolated from images if exist. The DLA task is difficult as there is no fixed layout for all documents, but instead, there are several layouts. There are various approaches for DLA for various different languages. In this paper, some of the previous techniques used in this field will be listed and then we will discuss the proposed method that depends on deep learning for documents’ text localization. We used Arabic Printed Text Image database (APTI [19]), ImageNet [18] and a dataset collected from different Arabic newspapers for training and evaluation.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2017 IEEE Eighth International Conference on Intelligent Computing and Information Systems (ICICIS 2017) |
| Place of Publication | Cairo, Egypt |
| Publisher | IEEE |
| Pages | 224-231 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781538608210 |
| ISBN (Print) | 9781538608227, 9772371723 |
| DOIs | |
| Publication status | Published - 5 Dec 2017 |
| Externally published | Yes |
| Event | 8th International Conference on Intelligent Computing and Information Systems - Ain Shams University, Cairo, Egypt Duration: 5 Dec 2017 → 7 Dec 2017 Conference number: 8 http://net2.asu.edu.eg/icicis/2017/ |
Publication series
| Name | |
|---|---|
| ISSN (Print) | 1687-1103 |
Conference
| Conference | 8th International Conference on Intelligent Computing and Information Systems |
|---|---|
| Abbreviated title | ICICIS 2017 |
| Country/Territory | Egypt |
| City | Cairo |
| Period | 5/12/17 → 7/12/17 |
| Internet address |
Keywords
- Optical character recognition
- Document layout analysis
- Font type recognition
- Font size recognition
- Deep learning
- Deep Convolutional Neural Networks (DCNN)
- Transfer learning (TL)
