Revolutionizing News Discovery: YOLOv7 Empowers Real-time Headline Extraction from Video Content
Keywords:News Discovery, Text Extraction, Image Segmentation, News Vidos, Deep Learning, YOLOv7
The rapid development of technology and communication channels has resulted in the rise of fake and manipulated video news headlines. This has led to a significant impact on the general public, with the potential of causing great harm and spreading misinformation. Therefore, there is a need to develop a system that can authenticate video news headlines and improve the overall quality of news media. This study presents a novel method for text extraction-based video news headline detection, which tackles the ever-changing landscape of news consumption. In an era where video is the primary news distribution channel, we focus our research on developing a productive system to recognize and extract relevant headlines from video news content. Our objective is to analyze text included in movies by applying sophisticated text extraction techniques, offering a way to produce accurate and succinct headlines. The methodology that has been suggested adeptly combines computer vision algorithms with natural language processing technologies to effectively navigate the complex visual and linguistic elements found in video news. In our quickly changing and fast-paced media ecosystem, our research helps to improve accessibility and user engagement with video news material by automating the headline extraction process, which also makes information retrieval easier. The results of this research can significantly improve news consumption in the digital era in terms of both efficacy and efficiency.
How to Cite
This is an open Access Article published by Research Center of Computing & Biomedical Informatics (RCBI), Lahore, Pakistan under CCBY 4.0 International License