Natural Scene Text Detection Based on MSER

DOI

10.2991/cimns-18.2018.21How to use a DOI?

Keywords

text detection; maximally stable extremal regions; convolution neural network; hierarchical clustering

Abstract

Scene text detection has important applications in the fields of intelligent transportation, industrial automation, multimedia retrieval and so on. This paper employs the improved MSER algorithm combined with convolutional neural network for scene text detection. Gradient amplitude enhancement processing is used to enhance the text boundary before a combinational suppression strategy is applied to filter out coincident regions, approximately coincident regions and nested regions. Then the Char-CNN classifier is designed to classify the candidate regions. A hierarchical clustering algorithm is used to merge the candidate regions, and finally generate the text position information. We evaluate the algorithm performance on the ICDAR2013 dataset. The results show that the improved MSER algorithm increases the recall rate of the character region from 88.9% to 90.2%, and the proportion of character regions in the candidate regions increases from 3.25% to 35.19%. And the classification accuracy of Char-CNN is 93.6%. The recall and accuracy rate of the algorithm are 0.68 and 0.85 respectively, and the F-Measure value is 0.76. Compared with existing scene text detection algorithms, the proposed algorithm has a competitive overall performance.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Kun Wang
AU  - Guokuan Li
AU  - Xujun Liu
AU  - Jingkun Yan
AU  - Shuli Li
AU  - Hao Huang
PY  - 2018/11
DA  - 2018/11
TI  - Natural Scene Text Detection Based on MSER
BT  - Proceedings of the 2018 3rd International Conference on Communications, Information Management and Network Security (CIMNS 2018)
PB  - Atlantis Press
SP  - 92
EP  - 95
SN  - 2352-538X
UR  - https://doi.org/10.2991/cimns-18.2018.21
DO  - 10.2991/cimns-18.2018.21
ID  - Wang2018/11
ER  -