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Gradient-angular-features for word-wise video script identification

Script identification at the word level is challenging because of complex backgrounds and low resolution of video. The presence of graphics and scene text in video makes the problem more challenging. In this paper, we employ gradient angle segmentation on words from video text lines. This paper pre...

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Main Authors: Shivakumara, P., Sharma, N., Pal, U., Blumenstein, M., Tan, C.L.
Format: Conference or Workshop Item
Language:English
Published: 2014
Subjects:
T Technology (General)
Online Access:http://eprints.um.edu.my/13090/1/gradient_angular_features_for_word.pdf
http://eprints.um.edu.my/13090/
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spelling my.um.eprints.130902015-03-24T01:35:01Z http://eprints.um.edu.my/13090/ Gradient-angular-features for word-wise video script identification Shivakumara, P. Sharma, N. Pal, U. Blumenstein, M. Tan, C.L. T Technology (General) Script identification at the word level is challenging because of complex backgrounds and low resolution of video. The presence of graphics and scene text in video makes the problem more challenging. In this paper, we employ gradient angle segmentation on words from video text lines. This paper presents new Gradient-Angular-Features (GAF) for video script identification, namely, Arabic, Chinese, English, Japanese, Korean and Tamil. This work enables us to select an appropriate OCR when the frame has words of multi-scripts. We employ gradient directional features for segmenting words from video text lines. For each segmented word, we study the gradient information in effective ways to identify text candidates. The skeleton of the text candidates is analyzed to identify Potential Text Candidates (PTC) by filtering out unwanted text candidates. We propose novel GAF for the PTC to study the structure of the components in the form of cursiveness and softness. The histogram operation on the GAF is performed in different ways to obtain discriminative features. The method is evaluated on 760 words of six scripts having low contrast, complex background, different font sizes, etc. in terms of the classification rate and is compared with an existing method to show the effectiveness of the method. We achieve 88.2% average classification rate. 2014-08 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.um.edu.my/13090/1/gradient_angular_features_for_word.pdf Shivakumara, P. and Sharma, N. and Pal, U. and Blumenstein, M. and Tan, C.L. (2014) Gradient-angular-features for word-wise video script identification. In: International Conference on Pattern Recognition (ICPR), 24-28 Aug 2014, Stockholm, Sweden. (Submitted)
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Shivakumara, P.
Sharma, N.
Pal, U.
Blumenstein, M.
Tan, C.L.
Gradient-angular-features for word-wise video script identification
description Script identification at the word level is challenging because of complex backgrounds and low resolution of video. The presence of graphics and scene text in video makes the problem more challenging. In this paper, we employ gradient angle segmentation on words from video text lines. This paper presents new Gradient-Angular-Features (GAF) for video script identification, namely, Arabic, Chinese, English, Japanese, Korean and Tamil. This work enables us to select an appropriate OCR when the frame has words of multi-scripts. We employ gradient directional features for segmenting words from video text lines. For each segmented word, we study the gradient information in effective ways to identify text candidates. The skeleton of the text candidates is analyzed to identify Potential Text Candidates (PTC) by filtering out unwanted text candidates. We propose novel GAF for the PTC to study the structure of the components in the form of cursiveness and softness. The histogram operation on the GAF is performed in different ways to obtain discriminative features. The method is evaluated on 760 words of six scripts having low contrast, complex background, different font sizes, etc. in terms of the classification rate and is compared with an existing method to show the effectiveness of the method. We achieve 88.2% average classification rate.
format Conference or Workshop Item
author Shivakumara, P.
Sharma, N.
Pal, U.
Blumenstein, M.
Tan, C.L.
author_facet Shivakumara, P.
Sharma, N.
Pal, U.
Blumenstein, M.
Tan, C.L.
author_sort Shivakumara, P.
title Gradient-angular-features for word-wise video script identification
title_short Gradient-angular-features for word-wise video script identification
title_full Gradient-angular-features for word-wise video script identification
title_fullStr Gradient-angular-features for word-wise video script identification
title_full_unstemmed Gradient-angular-features for word-wise video script identification
title_sort gradient-angular-features for word-wise video script identification
publishDate 2014
url http://eprints.um.edu.my/13090/1/gradient_angular_features_for_word.pdf
http://eprints.um.edu.my/13090/
_version_ 1643689459808468992
score 13.211869

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