Deep Residual Text Detection Network for Scene Text

被引：11

作者：

Zhu, Xiangyu ^{[1
]}

Jiang, Yingying ^{[1
]}

Yang, Shuli ^{[1
]}

Wang, Xiaobing ^{[1
]}

Li, Wei ^{[1
]}

Fu, Pei ^{[1
]}

Wang, Hua ^{[1
]}

Luo, Zhenbo ^{[1
]}

机构：

[1] Samsung R&D Inst China, Machine Learning Lab, Beijing, Peoples R China

来源：

2017 14TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR), VOL 1 | 2017年

关键词：

Scene text detection; Deep Residual Networks; CTPN;

D O I：

10.1109/ICDAR.2017.137

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Scene text detection is a challenging problem in computer vision. In this paper, we propose a novel text detection network based on prevalent object detection frameworks. In order to obtain stronger semantic feature, we adopt ResNet as feature extraction layers and exploit multi-level feature by combining hierarchical convolutional networks. A vertical proposal mechanism is utilized to avoid proposal classification, while regression layer remains working to improve localization accuracy. Our approach evaluated on ICDAR2013 dataset achieves 0.91 F-measure, which outperforms previous state-of-the-art results in scene text detection.

引用

页码：807 / 812

页数：6

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