Detecting arbitrary oriented text in the wild with a visual attention model

Wenyi Huang, Dafang He, Xiao Yang, Zihan Zhou, Daniel Kifer, C. Lee Giles

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

8 Scopus citations

Abstract

Text embedded in images provides important semantic information about a scene and its content. Detecting text in an unconstrained environment is a challenging task because of the many fonts, sizes, backgrounds, and alignments of the characters. We present a novel attention model for detecting arbitrary oriented and curved scene text. Inspired by the attention mechanisms in the human visual system, our model utilizes a spatial glimpse network to processes the attended area and deploys a recurrent neural network that aggregates the information over time to determine the attention movement. Combining this with an off-the-shelf region proposal method, the model achieves the state-of-the-art performance on the highly cited ICDAR2013 dataset, and the MSRA-TD500 dataset which contains arbitrary oriented text.

Original languageEnglish (US)
Title of host publicationMM 2016 - Proceedings of the 2016 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages551-555
Number of pages5
ISBN (Electronic)9781450336031
DOIs
StatePublished - Oct 1 2016
Event24th ACM Multimedia Conference, MM 2016 - Amsterdam, United Kingdom
Duration: Oct 15 2016Oct 19 2016

Publication series

NameMM 2016 - Proceedings of the 2016 ACM Multimedia Conference

Other

Other24th ACM Multimedia Conference, MM 2016
CountryUnited Kingdom
CityAmsterdam
Period10/15/1610/19/16

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All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Huang, W., He, D., Yang, X., Zhou, Z., Kifer, D., & Giles, C. L. (2016). Detecting arbitrary oriented text in the wild with a visual attention model. In MM 2016 - Proceedings of the 2016 ACM Multimedia Conference (pp. 551-555). (MM 2016 - Proceedings of the 2016 ACM Multimedia Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2964284.2967282