Int J Ambient Comput Intell (IJACI) 8(2):32–51Īwad A (2019) Denoising images corrupted with impulse Gaussian, or a mixture of impulse and Gaussian noise. Further, we also employed other well-known handcrafted feature-based and deep learning approaches for a comparison.Īcharjya D, Anitha A (2017) A comparative study of statistical and rough computing models in predictive data analysis. In our test results, we observed that the proposed CNN is coherent and robust enough to identify scripts in both scenarios, with and without noise. Besides, the effects of three common noises namely, Salt & pepper, Gaussian and Poisson were considered on the scripts along with their hybridized metamorphosis. For validation, we used a publicly available dataset named CVSI-15. In this work, a deep learning-based system, which we call LWSINet: LightWeight Script Identification Network (6-layered CNN) is proposed to identify video scripts. Other than that, video script identification is not trivial as we have difficult issues, such as low resolution, complex background, noise, and blur effects. Since Optical Character Recognition (OCR) engines are script-dependent, script identification is a precursor. Videos – a high volume of texts – broadcast via different media, such as television and the internet.
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