Name
Abstract | ||
|---|---|---|
Stamps and logos are generally used for authenticating the source of a document. For automatic document processing, identification and segmentation of stamps and logos are essential. In the past, methods to detect stamps and logos were limited to specific shapes, colors, or training data. However, stamps and logos can be of any shape or color. In this paper, we have proposed a novel stamp and logo detection technique. Our approach is based on the fact that stamps and logos, in general, are not the primary contents of a document. This fact motivates us to propose an outlier detection technique for the same purpose in a feature space. Based on some geometric features, the detected outliers are classified as stamps and logos. Our method shows good performance in case of separating them from text. Moreover, this technique is capable of detecting logos as well as chromatic and achromatic stamps. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/NCVPRIPG.2015.7489947 | 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) |
Keywords | Field | DocType |
stamp detection,logo detection,document images,document authentication,automatic document processing,stamp identification,stamp segmentation,logo identification,logo segmentation,outlier detection,feature space,geometric features,achromatic stamps | Anomaly detection,Computer vision,Feature vector,Authentication,Pattern recognition,Computer science,Segmentation,Document processing,Logos Bible Software,Outlier,Logo,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
2372-658X | 1 | 0.36 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soumyadeep Dey | 1 | 12 | 3.00 |
| Jayanta Mukhopadhyay | 2 | 72 | 26.05 |
| Shamik Sural | 3 | 1008 | 96.36 |