Deep TextSpotter: An End-To-End Trainable Scene Text Localization and Recognition Framework

Michal Busta, Lukas Neumann, Jiri Matas; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2204-2212

Abstract


A method for scene text localization and recognition is proposed. The novelties include: training of both text detection and recognition in a single end-to-end pass, the structure of the recognition CNN and the geometry of its input layer that preserves the aspect of the text and adapts its resolution to the data. The proposed method achieves state-of-the-art accuracy in the end-to-end text recognition on two standard datasets - ICDAR 2013 and ICDAR 2015, whilst being an order of magnitude faster than competing methods - the whole pipeline runs at 10 frames per second on an NVidia K80 GPU.

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[bibtex]
@InProceedings{Busta_2017_ICCV,
author = {Busta, Michal and Neumann, Lukas and Matas, Jiri},
title = {Deep TextSpotter: An End-To-End Trainable Scene Text Localization and Recognition Framework},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}