DEEP LEARNING-BASED AUTOMATIC VIDEO CAPTIONING FOR ENHANCED MULTIMEDIA ACCESSIBILITY

This paper presented a deep learning-based framework for automatic video captioning designed to enhance multimedia accessibility. The proposed approach combines visual feature extraction, temporal modeling, speech/audio processing, multimodal feature fusion, attention mechanisms, and neural language generation. Unlike conventional speech-to-text systems that primarily represent spoken language, the proposed framework considers both visual and auditory information. This multimodal approach can provide richer descriptions of video content and can support applications in education, social media, digital libraries, online communication, and public information systems. Although challenges remain in computational efficiency, caption accuracy, temporal consistency, multilingual support, and privacy, deep learning provides a promising foundation for developing scalable and accessible video-captioning technologies. Continued research in vision-language models, multimodal Transformers, real-time processing, and human-centered evaluation can further improve the usefulness of automatic video captioning systems.

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