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Detection of Frozen Video Subtitles Using Machine Learning
Linköping University, Department of Electrical Engineering, Computer Vision.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

When subtitles are burned into a video, an error can sometimes occur in the encoder that results in the same subtitle being burned into several frames, resulting in subtitles becoming frozen. This thesis provides a way to detect frozen video subtitles with the help of an implemented text detector and classifier.

Two types of classifiers, naïve classifiers and machine learning classifiers, are tested and compared on a variety of different videos to see how much a machine learning approach can improve the performance. The naïve classifiers are evaluated using ground truth data to gain an understanding of the importance of good text detection. To understand the difficulty of the problem, two different machine learning classifiers are tested, logistic regression and random forests.

The result shows that machine learning improves the performance over using naïve classifiers by improving the specificity from approximately 87.3% to 95.8% and improving the accuracy from 93.3% to 95.5%. Random forests achieve the best overall performance, but the difference compared to when using logistic regression is small enough that more computationally complex machine learning classifiers are not necessary. Using the ground truth shows that the weaker naïve classifiers would be improved by at least 4.2% accuracy, thus a better text detector is warranted. This thesis shows that machine learning is a viable option for detecting frozen video subtitles.

Place, publisher, year, edition, pages
2019. , p. 106
Keywords [en]
Machine learning, Text detection, Text localization, Text extraction, Frozen subtitles, Burnt-in subtitles, Hardcoded subtitles, Classification, Text classification, Frozen subtitle classification
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-158239ISRN: LiTH-ISY-EX--19/5206--SEOAI: oai:DiVA.org:liu-158239DiVA, id: diva2:1331490
External cooperation
Agama Technologies
Subject / course
Computer Vision Laboratory
Presentation
2019-06-04, Algoritmen, Linköping, 13:00 (English)
Supervisors
Examiners
Available from: 2019-06-27 Created: 2019-06-26 Last updated: 2019-06-27Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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