• DocumentCode
    118422
  • Title

    Thai text localization in natural scene images using Convolutional Neural Network

  • Author

    Kobchaisawat, Thananop ; Chalidabhongse, Thanarat H.

  • Author_Institution
    Dept. of Comput. Eng., Chulalongkorn Univ., Bangkok, Thailand
  • fYear
    2014
  • fDate
    9-12 Dec. 2014
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    Text detection in natural scene images is a challenging problem due to many variations and uncontrollable factors comparing to text detection on scanned document. Unlike the existing Thai text detection methods which focus on using connected component analysis combining with other rule-based techniques to localize text, our proposed method is based on a well-known automatic feature extractor neural networks called Convolutional Neural Networks (CNN). The CNN is first trained with both English and Thai text datasets. A multi-scaled text confidence maps are constructed in order to cope with the text size variations. Some post-processing and Thai text analysis are also employed to acquire text locations in the image. Base on our experimental results, the proposed method can detect English and Thai text from natural scene images with a promising accuracy comparing to the state-of-the-art method.
  • Keywords
    feature extraction; neural nets; text detection; CNN; English text datasets; Thai text analysis; Thai text datasets; Thai text detection methods; Thai text localization; automatic feature extractor neural networks; connected component analysis; convolutional neural network; multiscaled text confidence maps; natural scene images; text detection; text locations; Abstracts; Decision support systems; Detectors; Erbium; Feature extraction; Neural networks; Text analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA)
  • Conference_Location
    Siem Reap
  • Type

    conf

  • DOI
    10.1109/APSIPA.2014.7041775
  • Filename
    7041775