Scene text magnifier

Toshiki Nakamura Nakamura, Anna Zhu, Seiichi Uchida

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

Scene text magnifier aims to magnify text in natural scene images without recognition. It could help the special groups, who have myopia or dyslexia to better understand the scene. In this paper, we design the scene text magnifier through interacted four CNN-based networks: character erasing, character extraction, character magnify, and image synthesis. The architecture of the networks are extended based on the hourglass encoderdecoders. It inputs the original scene text image and outputs the text magnified image while keeps the background unchange. Intermediately, we can get the side-output results of text erasing and text extraction. The four sub-networks are first trained independently and fine-tuned in end-to-end mode. The training samples for each stage are processed through a flow with original image and text annotation in ICDAR2013 and Flickr dataset as input, and corresponding text erased image, magnified text annotation, and text magnified scene image as output. To evaluate the performance of text magnifier, the Structural Similarity is used to measure the regional changes in each character region. The experimental results demonstrate our method can magnify scene text effectively without effecting the background.

本文言語英語
ホスト出版物のタイトルProceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
出版社IEEE Computer Society
ページ825-830
ページ数6
ISBN(電子版)9781728128610
DOI
出版ステータス出版済み - 9月 2019
イベント15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 - Sydney, オーストラリア
継続期間: 9月 20 20199月 25 2019

出版物シリーズ

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

会議

会議15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
国/地域オーストラリア
CitySydney
Period9/20/199/25/19

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識

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