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Deep Graph Laplacian Hashing for Image Retrieval Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Multi-lingual Scene Text Detection Based on Fully Convolutional Networks

Authors : Shaohua Liu, Yan Shang, Jizhong Han, Xi Wang, Hongchao Gao, Dongqin Liu

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

In the paper, we propose a method based on transfer learning to detect multi-lingual text in natural scenes. First, a semantic segmentation map of the input image is obtained through a fully convolution network (FCN). In this map, each pixel is classified to text or none-text. And then, the candidate boxes of text regions are computed based on the map. In this procedure, VGG network is trained to obtain a basic character classifier of single language. Based on this VGG model, FCN has the ability to classify each pixel to text or none-text for multi-lingual with doing transfer learning. Finally, the bounding boxes of text are carry out by filtering the unsatisfied candidates with some rules. The experimental results show that our method achieves good performance on the task of multi-lingual text detection. And compared with other advanced method, the time cost of our method is shortest.

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Metadata
Title
Multi-lingual Scene Text Detection Based on Fully Convolutional Networks
Authors
Shaohua Liu
Yan Shang
Jizhong Han
Xi Wang
Hongchao Gao
Dongqin Liu
Copyright Year
2018
Book
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77379-7

Electronic ISBN: 978-3-319-77380-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77380-3_40