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.
Visual Script and Language Identification / Nicolaou, Anguelos; Bagdanov, Andrew D.; Gomez, Lluis; Karatzas, Dimosthenis. - ELETTRONICO. - (2016), pp. 393-398. (Intervento presentato al convegno 12th IAPR International Workshop on Document Analysis Systems, DAS 2016 tenutosi a grc nel 2016) [10.1109/DAS.2016.63].
Visual Script and Language Identification
BAGDANOV, ANDREW DAVID;
2016
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.
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