Paper Title
A Comprehensive Survey on Image Captioning
Article Identifiers
Registration ID: IJNRD_212037
Published ID: IJNRD2403291
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Keywords
Deep learning, Natural Language Processing, Image Captioning, Visual-language, ResNet, LSTM, Recurrent Neural Network, Convolutional Neural Network
Abstract
An image captioning system uses both computer vision and natural language processing modules. While the computer vision module identifies important items or extracts information from images, the Natural Language Processing (NLP) module accurately constructs syntactic and semantic picture captions. Due to its significance in real-world applications like the analysis of enormous amounts of raw photos and the detection of previously unidentified patterns for machine learning applications used to drive self-driving cars and create software that helps the blind, a lot of people have recently developed an interest in the automatic generation of a natural language description or text of an image. It integrates computer vision with natural language processing, two essential components of artificial intelligence. In this study, image captioning will be done using neural networks. ResNet is used as the encoder to retrieve the image data, To create subtitles for videos that use the built-in language, CNN is used as an encoder to retrieve data from the picture and neural convolutional network RNN (Long-Short-Term Memory) as a decoder.
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How To Cite (APA)
Shruti Biradar, Vipul Gaikwad, Harsh Pawar, Rohan Dupade, & Manjiri Ranjanikar (March-2024). A Comprehensive Survey on Image Captioning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), c757-c765. https://ijnrd.org/papers/IJNRD2403291.pdf
Issue
Volume 9 Issue 3, March-2024
Pages : c757-c765
Other Publication Details
Paper Reg. ID: IJNRD_212037
Published Paper Id: IJNRD2403291
Research Area: Computer Science & TechnologyÂ
Author Type: Indian Author
Country: Pune, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2403291.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403291
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