DeepLens: Integrating Deep Learning for Image Captioning and Hashtag Generation
MR. G. NUTAN KUMAR
Assistant Professor, Dept. of Information Technology, Sreenidhi Institute of Science and Technology
DR. K. KRANTHI KUMAR
Associate Professor, Dept. of Information Technology, Sreenidhi Institute of Science and Technology
K. PAVAN
B.Tech Student, Dept. of Information Technology, Sreenidhi Institute of Science and Technology 20311a12a5@sreenidhi.edu.in
V. VIHAR
B.Tech Student, Dept. of Information Technology, Sreenidhi Institute of Science and Technology 20311a12b6@sreenidhi.edu.in
K. KARTHIKEYA
B.Tech Student, Dept. of Information Technology, Sreenidhi Institute of Science and Technology 20311a12a7@sreenidhi.edu.in
Abstract - In this research, we introduce a groundbreaking methodology that leverages deep learning techniques to revolutionize the process of generating descriptive captions and hashtags for images, effectively bridging the gap between computer vision and natural language understanding. Traditional approaches to image caption generation have often relied on rudimentary techniques such as handcrafted features and rule-based systems, which inherently struggle to capture the intricate semantics of images and adapt to diverse datasets. Recognizing these limitations, our novel framework integrates state-of-the-art convolutional neural networks (CNNs) for precise image feature extraction and recurrent neural networks (RNNs), specifically employing the ResNet-50 architecture, for seamless sequence generation. Furthermore, in addition to traditional evaluation metrics such as BLEU scores and human assessments, our system, DeepLens, introduces a groundbreaking feature: automatic hashtag generation. By meticulously analyzing both the content and context of images, DeepLens autonomously generates hashtags that enrich social media content sharing and engagement, presenting a novel paradigm in the realm of image captioning. In addition to its advancements in captioning accuracy and user experience enhancement, DeepLens offers scalability and adaptability to various domains. Its robust architecture allows for seamless integration with different datasets and environments, making it versatile for a wide range of applications. Through this comprehensive approach, we aim to not only enhance the accuracy and relevance of generated captions but also elevate the overall user experience in navigating and interacting with visual content across various platforms and applications.
