Name
Abstract | ||
|---|---|---|
In this paper, we present a novel approach for segmentation-based handwritten keyword spotting. The proposed approach relies upon the extraction of a simple yet efficient descriptor which is based on projections of oriented gradients. To this end, a global and a local word image descriptors, together with their combination, are proposed. Retrieval is performed using to the euclidean distance between the descriptors of a query image and the segmented word images. The proposed methods have been evaluated on the dataset of the ICFHR 2014 Competition on handwritten keyword spotting. Experimental results prove the efficiency of the proposed methods compared to several state-of-the-art techniques. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/DAS.2016.61 | 2016 12th IAPR Workshop on Document Analysis Systems (DAS) |
Keywords | Field | DocType |
Word Spotting,Feature Extraction,Projections of Oriented Gradients | Computer vision,Pattern recognition,Computer science,Segmentation,Euclidean distance,Feature extraction,Keyword spotting,Artificial intelligence,Visual descriptors | Conference |
Citations | PageRank | References |
3 | 0.36 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| George Retsinas | 1 | 8 | 3.91 |
| Georgios Louloudis | 2 | 81 | 9.54 |
| Nikolaos Stamatopoulos | 3 | 20 | 2.79 |
| Basilis Gatos | 4 | 773 | 43.34 |