Scene Text Relocation with Guidance

Anna Zhu, Seiichi Uchida

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

Applying object proposal technique for scene text detection becomes popular for its significant improvement in speed and accuracy for object detection. However, some of the text regions after the proposal classification are overlapped and hard to remove or merge. In this paper, we present a scene text relocation system that refines the detection from text proposals to text. An object proposal-based deep neural network is employed to get the text proposals. To tackle the detection overlapping problem, a refinement deep neural network relocates the overlapped regions by estimating the text probability inside, and locating the accurate text regions by thresholding. Since the spacebetweenwordsindifferenttextlinesarevarious, aguidance mechanism is proposed in text relocation to guide where to extract the text regions in word level. This refinement procedure helps boost the precision after removing multiple overlapped text regions or joint cracked text regions. The experimental results on standard benchmark ICDAR 2013 demonstrate the effectiveness of the proposed approach.

本文言語英語
ホスト出版物のタイトルProceedings - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017
出版社IEEE Computer Society
ページ1289-1294
ページ数6
ISBN(電子版)9781538635865
DOI
出版ステータス出版済み - 1 25 2018
イベント14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017 - Kyoto, 日本
継続期間: 11 9 201711 15 2017

出版物シリーズ

名前Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
1
ISSN(印刷版)1520-5363

その他

その他14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017
Country日本
CityKyoto
Period11/9/1711/15/17

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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