NIL

Videos contain a high volume of texts and are broadcasted via different sources, such as television, the internet, etc. Since optical character recognition (OCR) engines are script-dependent, script identification is the precursor for them. Depending on the video sources, identification of video scripts is not trivial since we have difficult issues, such as low resolution, complex background, noise, blur effects, etc. In this work, a deep learning-based system named as LWSINet: LightWeight Script Identification Network (6-layered CNN) is proposed to identify the video scripts. For validation, we used a publicly available dataset named CVSI-15. Besides, the effects of the three common noises namely, Salt \& pepper, Gaussian, and Poisson were considered on the scripts along with their hybridized metamorphosis. In our test results, we observed that the proposed CNN is coherent and robust enough to identify scripts in scenarios: without and with noise. Further, we also employed other well-known handcrafted feature-based and deep learning techniques and obtained better results with the proposed framework