Visual semantic re-ranker for text spotting

Conference Article


Iberoamerican Congress on Pattern Recognition (CIARP)




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Many current state-of-the-art methods for text recognition are based on purely local information and ignore the semantic corre- lation between text and its surrounding visual context. In this paper, we propose a post-processing approach to improve the accuracy of text spotting by using the semantic relation between the text and the scene. We initially rely on an off-the-shelf deep neural network that provides a series of text hypotheses for each input image. These text hypotheses are then re-ranked using the semantic relatedness with the object in the image. As a result of this combination, the performance of the original network is boosted with a very low computational cost. The proposed framework can be used as a drop-in complement for any text-spotting algorithm that outputs a ranking of word hypotheses. We validate our approach on ICDAR’17 shared task dataset.


computer vision.

Scientific reference

A. Sabir, F. Moreno-Noguer and L. Padró. Visual semantic re-ranker for text spotting, 23rd Iberoamerican Congress on Pattern Recognition, 2018, , to appear.