Scene Text-Line Extraction with Fully Convolutional Network and Refined Proposals

Guan Xin Zeng, Yu Hong Hou, Po Chyi Su, Li Wei Kang

研究成果: 書貢獻/報告類型會議論文篇章

摘要

Texts appearing in images are often regions of interest. Locating such areas for further analysis can help to extract image-related information and facilitate many applications. Pixel-based segmentation and region-based object classification are two methodologies for identifying text areas in images and have their own pros and cons. In this research, a text detection scheme consisting of a pixel-based classification network and a supplemented region proposal network is proposed. The main network is a Fully Convolutional Network (FCN) employing Feature Pyramid Networks (FPN) and Atrous Spatial Pyramid Pooling (ASPP) to indicate possible text areas and text borders with high recall. Certain areas are further processed by the refinement network, i.e., a simplified Connectionist Text Proposal Network (CTPN) with high precision. Non-Maximum Suppression (NMS) is then applied to form appropriate text-lines. The experimental results show feasibility of the scheme.

原文英語
主出版物標題2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1247-1251
頁數5
ISBN(電子)9789881476883
出版狀態已發佈 - 2020 十二月 7
事件2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, 新西兰
持續時間: 2020 十二月 72020 十二月 10

出版系列

名字2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings

會議

會議2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
國家/地區新西兰
城市Virtual, Auckland
期間2020/12/072020/12/10

ASJC Scopus subject areas

  • 人工智慧
  • 電腦網路與通信
  • 電腦視覺和模式識別
  • 硬體和架構
  • 訊號處理
  • 決策科學(雜項)
  • 儀器

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