Image Captioning Engine: Using Deep Learning

Image captioning is a model development process has the ability to generate descriptive captions in an automatic format for images by learning visual features and mapping them to natural language sequences. In this task there are various techniques involved from the process of “computer vision” and “natural language processing”. It utilizes deep “neural networks” to connect the gap between visual presentation and textual description. “CNN (Convolutional neural network)” and “RNN (Recurrent Neural Networks)” are employed to extract spatial features from images and generate fluent, contextually appropriate captions respectively. More recently, Transformerbased architectures, such as those used in models like GPT and BERT, have enhanced the ability to produce more contextually relevant and fluent captions. Image captioning comprises with wide spectrum of applications, from assisting the “visually impaired” and improving content retrieval systems to enhancing human-computer interaction and automating social media analysis.

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Publication Details

Published
2025-04-04
DOI
https://doi.org/10.1109/iccsai64074.2025.11064687
Primary Topic
Multimodal Machine Learning Applications
Type
article
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Image Captioning Engine: Using Deep Learning

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Multimodal Machine Learning Applications
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Image Captioning Engine: Using Deep Learning

Charu Awasthi, Sukanta Dash, Vanshaj Bhatnagar, Pritam Singh, Sonia Yadav -, Sarthak Razdan
article en

Abstract

Image captioning is a model development process has the ability to generate descriptive captions in an automatic format for images by learning visual features and mapping them to natural language sequences. In this task there are various techniques involved from the process of “computer vision” and “natural language processing”. It utilizes deep “neural networks” to connect the gap between visual presentation and textual description. “CNN (Convolutional neural network)” and “RNN (Recurrent Neural Networks)” are employed to extract spatial features from images and generate fluent, contextually appropriate captions respectively. More recently, Transformerbased architectures, such as those used in models like GPT and BERT, have enhanced the ability to produce more contextually relevant and fluent captions. Image captioning comprises with wide spectrum of applications, from assisting the “visually impaired” and improving content retrieval systems to enhancing human-computer interaction and automating social media analysis.

Jaypee Institute of Information Technology (IN)
Quality Education
Openalex Percentile: Top 85%
Multimodal Machine Learning Applications
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