Smart Embedded Device for Object and Text Recognition through Real Time Video Using Raspberry PI

  • Authors

    • B. Anil Kumar
    • T. Praneeth Chowdary
    • T. Govinda Rao
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.27959
  • Object Detection, Raspberry Pi, SSD, Text extraction, Video frame extraction.

  • Abstract

    Object recognition, text recognition, face recognition, navigation is a challenging problem in real world scenario particularly in developing advanced technology to assist for people. The complexity of recognition for a system is difficult because of having the objects and texts are having variations in sizes, shapes, mixed with complex backgrounds and having different lighting condition.We proposed a smart embedded device that consists of five switches to recognize objects and text information from videos, images, documents and pdf files. For recognizing the object, the image data is captured by using pi camera and is processed on Raspberry pi by using SSD method for detecting objects in captured data by a single deep neural network to provide a fixed size bunch of bounding boxes and scores for the presence of object class instances in those boxes.By combining MobileNets architecture with the single shot detector framework the prediction of accuracy in detecting object is more and fast. The text information from videos are recognized by extracting a best frame using Laplacian method and performs pre-processing on the frame by applying noise removal methods. Thresholding methods are applied to improve the lucidity of the text area and Grab-cut approach is used to eliminate the unwanted backgrounds. The frame is then given to the OCR to extract the text information and was given to TTS converter to convert the text output into speech from to assist users easily.

     

     

     

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  • How to Cite

    Anil Kumar, B., Praneeth Chowdary, T., & Govinda Rao, T. (2018). Smart Embedded Device for Object and Text Recognition through Real Time Video Using Raspberry PI. International Journal of Engineering & Technology, 7(4.19), 556-562. https://doi.org/10.14419/ijet.v7i4.19.27959

    Received date: 2019-02-26

    Accepted date: 2019-02-26

    Published date: 2018-11-27