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arXiv · 1810.09776

Visual Semantic Re-ranker for Text Spotting

Ahmed Sabir·Francesc Moreno-Noguer·Lluís Padró
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Abstract

Many current state-of-the-art methods for text recognition are based on purely local information and ignore the semantic correlation 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.

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Ahmed Sabir, Francesc Moreno-Noguer, Lluís Padró. 2018-10-23. Visual Semantic Re-ranker for Text Spotting. https://arxiv.org/abs/1810.09776

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