• DocumentCode
    174205
  • Title

    A robust approach to the recognition of text based Bangla road sign

  • Author

    Banik, Bithi ; Alam, Fahim Irfan

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Univ. of Chittagong, Chittagong, Bangladesh
  • fYear
    2014
  • fDate
    23-24 May 2014
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    Road sign recognition is considered to be one of the most fascinating and interesting field of research in intelligent vehicle and machine learning. Road signs are typically placed either by the roadside or above roads. They provide important information in order to make driving safer and easier. This paper proposes an algorithm that recognizes Bangla road sign with a better percentage. The algorithm starts with capture image from real video scene, text detection from images, character segmentation and recognition of characters through shape matrix. The constructed feature vectors for each individual Bangla road sign are learned into a neural network which later classifies new instance of Bangla road sign. The promising preliminary experimental results indicate a positive potential of our algorithm.
  • Keywords
    driver information systems; feature extraction; image classification; image segmentation; matrix algebra; natural language processing; optical character recognition; road traffic; text analysis; vectors; video signal processing; character recognition; character segmentation; feature vectors; intelligent vehicle; machine learning; neural network; real video scene; shape matrix; text based Bangla road sign recognition; text detection; Character recognition; Feature extraction; Image edge detection; Image segmentation; Roads; Shape; Text recognition; Bangla road sign recognition; bag of words; character recognition; character segmentation; text detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Informatics, Electronics & Vision (ICIEV), 2014 International Conference on
  • Conference_Location
    Dhaka
  • Print_ISBN
    978-1-4799-5179-6
  • Type

    conf

  • DOI
    10.1109/ICIEV.2014.6850852
  • Filename
    6850852