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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.

Photographs have become a cost-effective solution for digitizing real-world data, especially for documents. The rapid expansion of multimedia information has significantly increased the demand for efficient document image processing.

This dataset consists of both non-retinal detachment and rhegmatogenous retinal detachment fundus images.  The fundus images were collected from the four eye hospital in the country (namely India) such as Silchar medical college and hospital (Assam), Aravind eye hospital (Tamil Nadu), LV prasad eye hospital (Hyderabad), and Medanta- The medicity (Gurugram).  A total of 1693 images have been collected from these hospitals of which 1017 fundus images belonged to retinal detachments and the rest 676 were non-retinal detachments.

In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment. As a result, full-reference (FR) and reduced-reference (RR) image quality assessment (IQA) methods are generally infeasible. Although no-reference (NR) methods are readily applicable, their performance is often not reliable.