This paper addresses the problem of detecting and recognizing text in images acquired ‘in the wild’. This is a severely under-constrained problem which needs to tackle a number of challenges including large occlusions, changing lighting conditions, cluttered backgrounds and different font types and sizes. In order to address this problem we leverage on recent and successful developments in the cross-fields of machine learning and natural language understanding. In particular, we initially rely on off-the-shelf deep networks already trained with large amounts of data and that provide a series of text hypotheses per input image. The outputs of this network are then combined with different priors obtained from both the semantic interpretation of the image and from a scene-based language model. As a result of this combination, the performance of the original network is consistently boosted. We validate our approach on ICDAR'17 shared task dataset.
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