Fully Convolutional Networks for Text Understanding in Scene Images

Dena Bazazian

Abstract

Text understanding in scene images has gained plenty of attention in the computer vision community and it is an important task in many applications as text carries semantically  rich  information  about  scene  content  and  context.   For  instance, reading text in a scene can be applied to autonomous driving, scene understanding or assisting visually impaired people. The general aim of scene text understanding is to localize and recognize text in scene images. Text regions are first localized in the original image by a trained detector model and afterwards fed into a recognition module. The tasks of localization and recognition are highly correlated since an inaccurate localization can affect the recognition task. The main purpose of this thesis is to devise efficient methods for scene text understanding. We investigate how the latest results on deep learning can advance text understanding pipelines. Recently, Fully Convolutional Networks (FCNs) and derived methods have achieved a significant performance on semantic segmentation and pixel level classification tasks. Therefore, we took benefit of the strengths of FCN approaches in order to detect and recognize text in natural scenes images.

Keywords

Text Understanding, Text Detection, Word Spotting, Fully Convolutional Network (FCN), Scene Images.

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