Name
Abstract | ||
|---|---|---|
Signature and logo as a query are important for content-based document image retrieval from a scanned document repository. This paper deals with signature and logo detection from a repository of scanned documents, which can be used for document retrieval using signature or logo information. A large intra-category variance among signature and logo samples poses challenges to traditional hand-crafted feature extraction-based approaches. Hence, the potential of deep learning-based object detectors namely, Faster R-CNN and YOLOv2 were examined for automatic detection of signatures and logos from scanned administrative documents. Four different network models namely ZF, VGG16, VGG
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub>
, and YOLOv2 were considered for analysis and identifying their potential in document image retrieval. The experiments were conducted on the publicly available "Tobacco-800" dataset. The proposed approach detects Signatures and Logos simultaneously. The results obtained from the experiments are promising and at par with the existing methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICFHR-2018.2018.00079 | 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) |
Keywords | Field | DocType |
Faster R-CNN, Deep Learning, Document retrieval, Signature detection, Logo detection | Signature detection,Pattern recognition,Computer science,Image retrieval,Feature extraction,Logo,Artificial intelligence,Document retrieval,Deep learning,Network model | Conference |
ISSN | ISBN | Citations |
2167-6445 | 978-1-5386-5876-5 | 0 |
PageRank | References | Authors |
0.34 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nabin Sharma | 1 | 132 | 11.55 |
| Ranju Mandal | 2 | 31 | 4.28 |
| Rabi Sharma | 3 | 3 | 1.03 |
| Umapada Pal | 4 | 1477 | 139.32 |
| M. Blumenstein | 5 | 168 | 31.87 |