Text Extraction from Video Using Generative AI Models: A Multimodal Approach

Text plays a pivotal role in human communication and information exchange, making accurate extraction of text from videos essential for various applications, including transcription, video analysis, and content understanding. Traditional methods face significant challenges in real-world scenarios due to motion blur, varying text orientations, low resolution, and complex video backgrounds, which hinder recognition accuracy. To address these challenges, this work introduces a generative AI-based framework for video text extraction. By utilizing advanced vision-language models within a robust multi-stage pipeline, our approach ensures accurate detection and recognition of textual data. The framework integrates frame clarity evaluation, leveraging sharpness and edge detection algorithms to select high-quality frames for analysis. These frames are processed using generative AI models, capable of capturing contextual and visual semantics beyond conventional OCR capabilities. The system is designed as an end-to-end solution, supporting video recording, frame processing, and seamless text extraction with enhanced robustness against noise and image degradation. Extensive validation highlights the proposed system’s superior performance in handling diverse text styles, orientations, and dynamic video content. The integration of generative AI significantly improves text extraction accuracy and scalability, demonstrating its potential for applications in automated transcription, digital archiving, and intelligent multimedia systems.