Published Paper Details:

Videos and images are rich sources of valuable information, combining visuals, sounds, and textual elements to convey critical details. In videos, dynamic content such as moving objects and on-screen text often holds essential information, while images frequently embed textual data within their visual content. However, extracting specific text from these multimedia formats is a challenging task. Traditional manual methods, such as pausing videos and editing images, are time-consuming and inefficient, and the extracted text is often not readily editable. To address these challenges, advanced Deep Learning techniques integrated with cutting-edge Optical Character Recognition (OCR) technologies provide a robust solution. Tools such as Keras_OCR and PyTesseract employ deep learning models and OCR algorithms to accurately recognize and convert text from video frames and images into machine-readable, editable formats. This approach not only automates the text extraction process but also enhances accuracy and efficiency. By leveraging these technologies, users can seamlessly extract, save, and edit text-rich content from multimedia sources, making it a valuable resource for applications in education, research, and analysis. This study highlights the transformative potential of combining deep learning with OCR technologies to unlock the hidden textual information in videos and images, enabling greater access to knowledge in the digital age.