Abstract
In most of the countries of the world, before a film is displayed in a theatre, it is mandatory to receive permission from the censor board. The job of a censor board is to evaluate a film, check for inappropriate content, judge the film and assign a rating to the film. In the present, human beings watch a film and rate the film based on how appropriate it would be to display the film to the general audience. With the growing range of application of image and text processing the objective of this research paper is to present how machine learning can be used to develop these two technologies can be used to rate films without the need of human beings. The goal of this paper is to develop an efficient as well as an accurate automated film censoring system which can detect explicit languages which have been used in films that detect inappropriate visual content and it assigns a rating such as ‘Universal’, ‘Universal Adult’ or ‘Adult’ based on the density of sensitive visual content and inappropriate language using the method of machine learning. The result of this paper would be a computer application which accepts the movie file as an input that produces the rating of the film as an output. This technology can be evolved into a mobile application which can accept videos stored on the mobile phone and assign a rating to them.
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The authors are grateful to Department of Information and Communication Technology, DAIICT, SRM University and, Pandit Deendayal Petroleum University for the permission to publish this research.
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All the authors make substantial contribution in this manuscript. KJ, MC, HP and MS participated in drafting the manuscript. KJ and MC wrote the main manuscript, all the authors discussed the results and implication on the manuscript at all stages.
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Jani, K., Chaudhuri, M., Patel, H. et al. Machine learning in films: an approach towards automation in film censoring. J. of Data, Inf. and Manag. 2, 55–64 (2020). https://doi.org/10.1007/s42488-019-00016-9
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DOI: https://doi.org/10.1007/s42488-019-00016-9