Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

Sheng-Ye Yan; Xin-Xing Xu; Qing-Shan Liu

doi:10.1007/s11633-014-0833-2

Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

doi: 10.1007/s11633-014-0833-2

1.
School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2.
School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore

基金项目:

This work was supported by National Natural Science Foundation of China (Nos. 61300163, 61125106 and 61300162) and Jiangsu Key Laboratory of Big Data Analysis Technology.

详细信息

通讯作者: Sheng-Ye Yan

中图分类号:

Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

1.
School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2.
School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore

Funds:

This work was supported by National Natural Science Foundation of China (Nos. 61300163, 61125106 and 61300162) and Jiangsu Key Laboratory of Big Data Analysis Technology.

More Information

Corresponding author: Sheng-Ye Yan

摘要: This paper proposes a new two-phase approach to robust text detection by integrating the visual appearance and the geometric reasoning rules. In the first phase, geometric rules are used to achieve a higher recall rate. Specifically, a robust stroke width transform (RSWT) feature is proposed to better recover the stroke width by additionally considering the cross of two strokes and the continuousness of the letter border. In the second phase, a classification scheme based on visual appearance features is used to reject the false alarms while keeping the recall rate. To learn a better classifier from multiple visual appearance features, a novel classification method called double soft multiple kernel learning (DS-MKL) is proposed. DS-MKL is motivated by a novel kernel margin perspective for multiple kernel learning and can effectively suppress the influence of noisy base kernels. Comprehensive experiments on the benchmark ICDAR2005 competition dataset demonstrate the effectiveness of the proposed two-phase text detection approach over the state-of-the-art approaches by a performance gain up to 4.4% in terms of F-measure.
- Text detection /
- geometric rule /
- stroke width transform (SWT) /
- support vector machine (SVM) /
- multiple kernel learning (MKL)
Abstract: This paper proposes a new two-phase approach to robust text detection by integrating the visual appearance and the geometric reasoning rules. In the first phase, geometric rules are used to achieve a higher recall rate. Specifically, a robust stroke width transform (RSWT) feature is proposed to better recover the stroke width by additionally considering the cross of two strokes and the continuousness of the letter border. In the second phase, a classification scheme based on visual appearance features is used to reject the false alarms while keeping the recall rate. To learn a better classifier from multiple visual appearance features, a novel classification method called double soft multiple kernel learning (DS-MKL) is proposed. DS-MKL is motivated by a novel kernel margin perspective for multiple kernel learning and can effectively suppress the influence of noisy base kernels. Comprehensive experiments on the benchmark ICDAR2005 competition dataset demonstrate the effectiveness of the proposed two-phase text detection approach over the state-of-the-art approaches by a performance gain up to 4.4% in terms of F-measure.
- Text detection /
- geometric rule /
- stroke width transform (SWT) /
- support vector machine (SVM) /
- multiple kernel learning (MKL)

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Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

doi: 10.1007/s11633-014-0833-2

通讯作者: Sheng-Ye Yan

Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

Corresponding author: Sheng-Ye Yan

计量

Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

doi: 10.1007/s11633-014-0833-2

1. School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China;

2. School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore

通讯作者: Sheng-Ye Yan

English Abstract

Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

1. School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China;

2. School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore

Corresponding author: Sheng-Ye Yan

全文HTML

目录

Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

doi: 10.1007/s11633-014-0833-2

通讯作者: Sheng-Ye Yan

Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

Corresponding author: Sheng-Ye Yan

计量

出版历程

Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

doi: 10.1007/s11633-014-0833-2

1. School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2. School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore

通讯作者: Sheng-Ye Yan

English Abstract

Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

1. School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2. School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore

Corresponding author: Sheng-Ye Yan

全文HTML

目录

1. School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China;

2. School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore

1. School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China;

2. School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore