A SURVEY ON VIDEO SCENE TEXT DEBLURRING USING TEXT-SPECIFIC MULTISCALE DICTIONARIES

Authors

  • Puja Chavan Department of computer engineering, VACOE, Ahmednagar, University of Pune, Maharashtra, India
  • Abhijeet Cholke Department of Computer Technology, P.Dr.V.V. Patil Institute of Tech. & Engg.(Polytechnic), Loni, MSBTE, Maharashtra, India
  • Prabhudev S Department of computer engineering, VACOE, Ahmednagar, University of Pune, Maharashtra, India

Keywords:

Scene Text, Text detection, Image De-blurring, multi-scale dictionaries

Abstract

This paper is concerned with the study of scene text detection and recognition from blurry natural video scene i.e. blurred images extracted from video which is important for image related purposes. In digital imaging, it’s a challenging task to restore a clear image from a single motion-blurred image due to camera shake. E.g. includes image retrieval, comparative studies of different images, as well as the tool that aid visually impaired individuals to access the pictorial information. Particularly when we use handheld cameras to capture natural scene images, a general problem, i.e., blur, frequently happens. There are several techniques available for text detection and recognition. For representation of the text and non-text fields, a string of text-specific multi-scale dictionaries (TMD) and a natural scene dictionary is used separately. The TMD-based text field reconstruction makes easier to deal with the different scales of strings in a blurry image effectively. A lot of work has been done for detecting text in images and a lot has to be done. In this survey, we extend an existing end-to-end solution for text detection and recognition in natural images to video.

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Published

2021-03-27

Issue

2015: ICITDCEME

Section

Articles

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How to Cite

Puja Chavan, Abhijeet Cholke, & Prabhudev S. (2021). A SURVEY ON VIDEO SCENE TEXT DEBLURRING USING TEXT-SPECIFIC MULTISCALE DICTIONARIES . International Journal of Innovations in Engineering Research and Technology, 1-5. https://repo.ijiert.org/index.php/ijiert/article/view/665