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The rapid progress in artificial intelligence has led to the development of automatic image captioning, which plays a vital role in computer vision and natural language processing. An image caption generator utilizes advanced deep learning techniques to produce descriptions that are both meaningful and contextually appropriate for images. This paper introduces a method that combines convolutional neural networks (cnn) for extracting features and recurrent neural networks (rnn), specifically long short-term memory (lstm) networks, for generating text. The suggested model analyses images to identify visual characteristics, which are subsequently linked to a language model to generate descriptive captions. We delve into the process of selecting the dataset, preprocessing techniques, model architecture, training process, and evaluation metrics employed to evaluate the system's performance. The experimental findings suggest that our approach successfully produces accurate and human-like captions, showcasing its potential for use in accessibility, content indexing, and automated annotation systems.
Keywords:
Image Captioning, Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory, Computer Vision, Natural Language Processing.
Cite Article:
"Image Caption Generator", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b584-b590, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504172.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator