Name
Abstract | ||
|---|---|---|
Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set and a simple end-to-end pipeline demonstrate State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICDAR.2015.7333855 | International Conference on Document Analysis and Recognition |
Field | DocType | Volume |
Computer vision,Pattern recognition,Segmentation,Computer science,Classification scheme,Local binary patterns,Sampling (statistics),Artificial intelligence,Operator (computer programming),Local feature descriptor | Journal | abs/1504.06133 |
ISSN | Citations | PageRank |
1520-5363 | 9 | 0.43 |
References | Authors | |
18 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anguelos Nicolaou | 1 | 104 | 10.14 |
| Andrew D. Bagdanov | 2 | 861 | 52.78 |
| Marcus Liwicki | 3 | 33 | 1.70 |
| Dimosthenis Karatzas | 4 | 406 | 38.13 |