Paper Contents
Abstract
This article explores the Access to video material is severely restricted globally by language hurdles, particularly in multilingual situations. The increasing demand for many languages cannot be met by traditional video localization methods like dubbing and subtitling, which are costly, time-consuming, and challenging to modify. In order to overcome this difficulty, this research suggests a novel method for automatic video localization that emphasizes straightforward integration and translation. We point out the primary drawbacks of the community-based methods now in use, such as the inability to match images, preserve linguistic context and accuracy, and enhance real-time processing. The research that we compare demonstrates the effectiveness of our suggested approach in terms of semantic efficiency and is a major advancement over the current real-time semantic method. By expanding language support and improving translation context, this automation system offers a scalable, economical, and successful means of bridging the language gap in video content, thereby increasing access to a worldwide audience.
Copyright
Copyright © 2024 Siddarthan Natarajan. This is an open access article distributed under the Creative Commons Attribution License.
