Follow the curve: Arbitrarily oriented scene text detection using key points spotting and curve prediction

Ke Yuan, Dafang He, Xiao Yang, Zhi Tang, Daniel Kifer, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Detecting arbitrarily oriented text in natural images is still a challenging and unsolved problem in multimedia. In this work, we propose an efficient and accurate scene text detector. The detector first detects key points which are carefully designed and semantically meaningful. Then the key points are learnt to be associated together to form a hexagon for each text instance. Starting from a key point, the detector then predicts curves alongside the border of the text region. Simple heuristic post-processing followed by the predicted curve resulting in more accurate text region prediction. The predicted key points are used as anchors to correct errors from the search process. The proposed method is efficient since it is a single stage key point detection method with simple post-processing. It is also effective and shows state-of-the-art or comparable performance on several benchmark datasets.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Multimedia and Expo, ICME 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728113319
DOIs
StatePublished - Jul 2020
Event2020 IEEE International Conference on Multimedia and Expo, ICME 2020 - London, United Kingdom
Duration: Jul 6 2020Jul 10 2020

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume2020-July
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2020 IEEE International Conference on Multimedia and Expo, ICME 2020
CountryUnited Kingdom
CityLondon
Period7/6/207/10/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

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