  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.
