Name
Abstract | ||
|---|---|---|
In this paper we introduce a script identification method based on hand-crafted texture features and an artificial neural network. The proposed pipeline achieves near state-of-the-art performance for script identification of video-text and state-of-the-art performance on visual language identification of handwritten text. More than using the deep network as a classifier, the use of its intermediary activations as a learned metric demonstrates remarkable results and allows the use of discriminative models on unknown classes. Comparative experiments in video-text and text in the wild datasets provide insights on the internals of the proposed deep network. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/DAS.2016.63 | 2016 12th IAPR Workshop on Document Analysis Systems (DAS) |
Keywords | Field | DocType |
LBP,neural networks,script identification,language identification,texture,scene-text,handwritten-text,video-text | Visual language,Computer science,Speech recognition,Natural language processing,Artificial intelligence,Language identification,Classifier (linguistics),Artificial neural network,Discriminative model | Journal |
Volume | Citations | PageRank |
abs/1601.01885 | 3 | 0.38 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anguelos Nicolaou | 1 | 104 | 10.14 |
| Andrew D. Bagdanov | 2 | 861 | 52.78 |
| Lluís Gómez | 3 | 93 | 8.74 |
| Dimosthenis Karatzas | 4 | 406 | 38.13 |