Image Caption Generator Using CNN and LSTM

In this have a look at, we discover the integration of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for the motive of image caption generation, a mission that involves a fusion of herbal language processing and computer imaginative and prescient techniques to describe images in English. Delving into the realm of photograph captioning, we meticulously investigate several fundamental concepts and methodologies associated with this area. Our technique includes leveraging prominent equipment inclusive of the Keras library, numpy, and Jupyter notebooks to facilitate the development of our studies. Furthermore, we delve into the utilization of the flickr_dataset and CNNs for image category, elucidating their significance in our examination. Through this research endeavor, we aim to make a contribution to the development of image captioning structures with the aid of combining modern-day strategies from both laptop imaginative and prescient and herbal language processing domain names.

Authors

Publication Details

Journal
International Journal of Innovative Science and Research Technology (IJISRT)
Published
2024-09-02
DOI
https://doi.org/10.38124/ijisrt/ijisrt24aug851
Citations
1
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.25
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article

Image Caption Generator Using CNN and LSTM

Monali Kapuriya, Zemi Lakkad, Satwi Shah
1 citations
International Journal of Innovative Science and Research Technology (IJISRT)
Multimodal Machine Learning Applications
0.25
article

Image Caption Generator Using CNN and LSTM

Monali Kapuriya, Zemi Lakkad, Satwi Shah
article en
1 citations

Abstract

In this have a look at, we discover the integration of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for the motive of image caption generation, a mission that involves a fusion of herbal language processing and computer imaginative and prescient techniques to describe images in English. Delving into the realm of photograph captioning, we meticulously investigate several fundamental concepts and methodologies associated with this area. Our technique includes leveraging prominent equipment inclusive of the Keras library, numpy, and Jupyter notebooks to facilitate the development of our studies. Furthermore, we delve into the utilization of the flickr_dataset and CNNs for image category, elucidating their significance in our examination. Through this research endeavor, we aim to make a contribution to the development of image captioning structures with the aid of combining modern-day strategies from both laptop imaginative and prescient and herbal language processing domain names.

International Journal of Innovative Science and Research Technology (IJISRT)
Quality Education
Openalex Percentile: Top 48%
Multimodal Machine Learning Applications
0.25
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Image Caption Generator Using CNN and LSTM — Monali Kapuriya, Zemi Lakkad, et al. · International Journal of Innovative Science and Research Technology (IJISRT) (2024) | TGRS Research Map | TGRS