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Video Retrieval Using Textual Queries and Spoken Text

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Techno-Societal 2018

Abstract

The expanded accessibility of broadband associations has prompted an increment in the utilization of Internet webcasting. Most webcasts are filed and got to various times reflectively. One test to skimming through such documents is the absence of text transcripts of the webcasts channel. Translation of addresses is a testing task, both in acoustic and in dialect illustrating. Recording lectures and putting them on the Web for access by under studies has turned into a general pattern at different colleges. To take full pick up of the information database that is manufactured by these records involved inquiry usefulness that must be given that goes past pursuit on meta-information level, however, performs an itemized examination of the relating multimedia reports. Videos and Texts demonstrated in lecture are nearly identified with the substance of the lecture, gives an important source for recovering lecture videos and indexing. Text substance may be separated, then analyze and deducted consequently by OCR (Optical Character Recognition) strategies. In this paper, for remedying lapses in the OCR Transcriptions, we investigated two separate systems connected just to unmatched question words. In any case, methodology delivers another arrangement of n-gram strings to coordinate the unedited OCR Transcriptions. These n-grams incorporate strings with an altered separation of 1 character and all conceivable n-gram substrings with no less than 3 characters. The second system for redressing OCR includes the word reference of spelling adjustment strategy gave in MS Words. The peculiarities of MS Word 2000, an OCR perceived string was extended through an application program interface into its corrected spellings. An exceptionally progressive style, just growing words that MS Word had flagged as erroneously spelled which we have depicted before. This significantly decreased the number of spurious word competitors and maintained a strategic distance from false matches.

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References

  1. Haubold A, Kender JR (2005) Augmented segmentation and visualization for presentation videos. In: Proceedings of the 13th Annual ACM international conference multimedia, pp 51–60

    Google Scholar 

  2. H€urst W, Kreuzer T, Wiesenh€utter M (2002) A qualitative study towards using large vocabulary automatic speech recognition to index recorded presentations for search and access over the web. In: Proceedings of the IADIS international conference WWW/Internet, pp 135–143

    Google Scholar 

  3. Kwang Kwang In Kim, Keechul Jung, Jin Hyung Kim Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. IEEE Trans Pattern Anal Mach Intell 25(12):1631–1639

    Google Scholar 

  4. Datong chen (2001) Text identification in complex Background Using SVM. IEEE

    Google Scholar 

  5. Moradi M, Mozaffari S (2013) Hybrid approach for Farsi/Arabic text detection and localisation in video frames. IET Image Process 7(2):154–164. https://doi.org/10.1049/iet-ipr.2012.0441

    Article  Google Scholar 

  6. Mosleh A (2013) Automatic inpainting scheme for video text detection and removal. IEEE Trans Image Process 22(11):4460–4472

    Article  MathSciNet  Google Scholar 

  7. Leeuwis E, Federico M, Cettolo M (2003) Language modeling and transcription of the ted corpus lectures, in Proceedings of the IEEE international conference acoustics, speech signal processing, pp 232–235

    Google Scholar 

  8. Lee D, Lee GG A Korean spoken document retrieval system for lecture search

    Google Scholar 

  9. Pong T-C, Wang F, Ngo C-W (2008) Structuring low-quality videotaped lectures for cross-reference browsing by video text analysis. J Pattern Recog 41(10):3257–3269

    Article  Google Scholar 

  10. Repp S, Gross A, Meinel C (2008) Browsing within lecture videos based on the chain index of speech transcription. IEEE Trans Learn Technol 1(3):145–156

    Article  Google Scholar 

  11. Haojin Yang, Christoph Meinel Content based lecture video retrieval using speech and video text information. IEEE Trans Learn Technol 7(2), April–June 2014

    Google Scholar 

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Sukhadeo, B.S., Subhash, R.A. (2020). Video Retrieval Using Textual Queries and Spoken Text. In: Pawar, P., Ronge, B., Balasubramaniam, R., Vibhute, A., Apte, S. (eds) Techno-Societal 2018 . Springer, Cham. https://doi.org/10.1007/978-3-030-16848-3_7

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