Scene Text Detection with Inception Text Proposal Generation Module

被引:1
|
作者
Zhang, Hang [1 ,2 ]
Liu, Jiahang [1 ]
Chen, Tieqiao [1 ,2 ]
机构
[1] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
来源
ICMLC 2019: 2019 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING | 2019年
关键词
Text detection; convolutional neural network; region proposal network; natural images;
D O I
10.1145/3318299.3318373
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Most scene text detection methods based on deep learning are difficult to locate texts with multi-scale shapes. The challenges of scale robust text detection lie in two aspects: 1) scene text can be diverse and usually exists in various colors, fonts, orientations, languages, and scales in natural images. 2) Most existing detectors are difficult to locate text with large scale change. We propose a new Inception-Text module and adaptive scale scaling test mechanism for multi-oriented scene text detection. the proposed algorithm enhances performance significantly, while adding little computation. The proposed method can flexibly detect text in various scales, including horizontal, oriented and curved text. The proposed algorithm is evaluated on three recent standard public benchmarks, and show that our proposed method achieves the state-of-the-art performance on several benchmarks. Specifically, it achieves an F-measure of 93.3% on ICDAR2013, 90.47% on ICDAR2015 and 76.08%(1) on ICDAR2017 MLT.
引用
收藏
页码:456 / 460
页数:5
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