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Pyrboxes: An efficient multi-scale scene text detector with feature pyramids

文献类型：期刊论文

作者Sheng, Fenfen1,2; Chen, Zhineng1; Zhang, Wei3; Xu, Bo1
刊名PATTERN RECOGNITION LETTERS
出版日期2019-07-01
卷号125页码:228-234
关键词Scene text detection Multi-scale text detection Grouped pyramid module Efficient and effective
ISSN号0167-8655
DOI10.1016/j.patrec.2019.04.022
通讯作者Chen, Zhineng(zhineng.chen@ia.ac.cn)
英文摘要Scene text detection has attracted many researches due to its importance to various applications. However, current approaches could not keep a good balance between accuracy and speed, i.e., a high-performance accuracy but with a low processing speed, or vice-versa. In this paper, we propose a novel model, named PyrBoxes, for efficient and effective multi-scale scene text detection. PyrBoxes consists of an SSD-based backbone that utilizes deep layers with strong semantics to detect texts in various sizes, and a proposed grouped pyramid module that leverages basic layers to append detailed locations into detection. Most existing detectors discard features from the basic layers due to the efficiency issue. We argue these layers contain fine-grained information, which is complementary to high-level semantics. Based on this, the grouped pyramid module combines the basic layers recursively into a detection layer via a top-down partition and a bottom-up group. Extensive experiments on both horizontal and oriented benchmarks, including ICDAR2013 Focused Scene Text, ICDAR2015 Incidental Text and COCO-Text, demonstrate that PyrBoxes achieves state-of-the-art or highly competitive performance compared with baselines, while runs significantly faster at inference. Furthermore, by experimenting on another ChiTVText dataset, PyrBoxes shows great generality to Chinese and long text lines. By visualizing some qualitative results, as expected, PyrBoxes provides more accurate locations and reduces the rate of missed detections, especially for small-sized texts. (C) 2019 Elsevier B.V. All rights reserved.
资助项目National Natural Science Foundation of China[61772526] ; Beijing Science and Technology Program[Z171100002217015]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000482374500032
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Beijing Science and Technology Program
源URL[http://ir.ia.ac.cn/handle/173211/27329]  
专题数字内容技术与服务研究中心_远程智能医疗
自动化研究所_数字内容技术与服务研究中心
通讯作者Chen, Zhineng
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.JD AI Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Sheng, Fenfen,Chen, Zhineng,Zhang, Wei,et al. Pyrboxes: An efficient multi-scale scene text detector with feature pyramids[J]. PATTERN RECOGNITION LETTERS,2019,125:228-234.
APA Sheng, Fenfen,Chen, Zhineng,Zhang, Wei,&Xu, Bo.(2019).Pyrboxes: An efficient multi-scale scene text detector with feature pyramids.PATTERN RECOGNITION LETTERS,125,228-234.
MLA Sheng, Fenfen,et al."Pyrboxes: An efficient multi-scale scene text detector with feature pyramids".PATTERN RECOGNITION LETTERS 125(2019):228-234.

入库方式： OAI收割

来源：自动化研究所

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