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Automatic Video Scene Segmentation to Separate Script for OCR

Published on February 2014 by Bharatratna P. Gaikwad, Ramesh R. Manza, Ganesh Manza
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National Conference on Recent Advances in Information Technology
Foundation of Computer Science USA
NCRAIT - Number 1
February 2014
Authors: Bharatratna P. Gaikwad, Ramesh R. Manza, Ganesh Manza
008ec50c-28f3-4e95-b66d-0abcf97d356b

Bharatratna P. Gaikwad, Ramesh R. Manza, Ganesh Manza . Automatic Video Scene Segmentation to Separate Script for OCR. National Conference on Recent Advances in Information Technology. NCRAIT, 1 (February 2014), 9-15.

@article{
author = { Bharatratna P. Gaikwad, Ramesh R. Manza, Ganesh Manza },
title = { Automatic Video Scene Segmentation to Separate Script for OCR },
journal = { National Conference on Recent Advances in Information Technology },
issue_date = { February 2014 },
volume = { NCRAIT },
number = { 1 },
month = { February },
year = { 2014 },
issn = 0975-8887,
pages = { 9-15 },
numpages = 7,
url = { /proceedings/ncrait/number1/15138-1403/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Advances in Information Technology
%A Bharatratna P. Gaikwad
%A Ramesh R. Manza
%A Ganesh Manza
%T Automatic Video Scene Segmentation to Separate Script for OCR
%J National Conference on Recent Advances in Information Technology
%@ 0975-8887
%V NCRAIT
%N 1
%P 9-15
%D 2014
%I International Journal of Computer Applications
Abstract

In Text or character recognition in images or video frames is a difficult problem to achieve video data. This paper proposes improved template matching algorithm that applied for the automatic extraction of text from image and video frames. Optical character recognition using template matching is a system model that is useful to recognize the character, digits& special character by comparing two images of the alphabet. The objectives of this system model are to develop a model for the Optical Character Recognition (OCR) system and to implement the template matching algorithm in developing the system model . The template matching techniques are more profound to font and size variations of the characters than the feature classification methods. This system tested the 35 videos with 700 video frames for each video. Empirical result of this system precision rate is 91. 52% for automatic character gets recognized images and video frames. Experimental results show the relatively high accuracy of the new developed robust algorithm when it is tested on several size characters and text.

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Index Terms

Computer Science
Information Sciences

Keywords

Video Processing Text Detection Localization Tracking Segmentation Template Matching Ocr