International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
Deep Learning-Based Image Captioning: Integrating CNN-LSTM Architectures with Attention Mechanisms Utkarsh Khare, Shivam Mishra, Utkarsh Pratap Singh, Shubhangi Tiwari, Rishi Rajput, Vineet Agarwal Utkarsh Khare, Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, India Shivam Mishra, Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, India Utkarsh Pratap Singh, Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, India Shubhangi Tiwari, Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, India Rishi Rajput, Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, India Mr. Vineet Agarwal, Department of Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This paper presents the comprehensive development of an automated image caption generator utilizing state-of-the-art deep learning methodologies. The system effectively integrates Convolutional Neural Networks (CNNs) for high-level image feature extraction with Long Short-Term Memory (LSTM) networks for sequential natural language generation. This hybrid architecture enables the generation of coherent, contextually accurate captions that describe the content of input images. Leveraging the Flickr8k dataset, the model is trained and validated to demonstrate the seamless integration of computer vision and natural language processing (NLP)—two traditionally distinct areas of artificial intelligence.
In addition, the research underscores the importance of employing a robust and modular development pipeline, which involves meticulous dataset preprocessing, vocabulary construction, embedding layers tuning, and architecture optimization. The effectiveness of various training strategies, such as transfer learning, dropout regularization, and beam search decoding, is also discussed to fine-tune the balance between accuracy and computational efficiency. Moreover, the paper investigates the transformative impact of image captioning technologies across various sectors. In biomedicine, for instance, captioning can support the interpretation of X-rays and MRIs; in social platforms, it facilitates automatic content tagging; and in educational platforms, it provides visual description support for learning materials, thereby promoting digital inclusivity.
The core objective of the project is to enhance humancomputer interaction by enabling machines to interpret and verbalize visual information in a manner that closely resembles human understanding. This not only aids in improved information retrieval and content indexing but also paves the way for diverse real-world applications in domains such as ecommerce (automated product descriptions), biomedical diagnostics (interpreting medical imagery), assistive technologies (for the visually impaired), autonomous vehicles (scene understanding), and social media content moderation.
This project not only maps the breakthroughs and current limitations in neural network-based image captioning but also contributes to the broader AI research landscape by identifying future directions. These include the exploration of attention mechanisms, transformer architectures, multilingual captioning, and context-aware captioning using external knowledge sources. Ultimately, the project aspires to bridge the semantic gap between vision and language, offering a step forward in making machinegenerated descriptions more intuitive, accurate, and reflective of human cognitive processes.
A significant portion of the study is dedicated to addressing the implementation challenges, including issues related to data preprocessing, model overfitting, language diversity, and semantic alignment between visual features and textual representations. The paper also examines standard evaluation metrics used in image captioning tasks, such as BLEU, METEOR, ROUGE, and CIDEr, highlighting their strengths and limitations in measuring linguistic and contextual quality.
© 2025, IRJET
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Impact Factor value: 8.315
Key Words: Reform Image captioning, deep learning, CNN-LSTM, Flickr8k dataset, natural language processing, accessibility, e-commerce, autonomous systems.
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