Home > Computer Science > Other > Volume-3 > Issue-5 > Natural Language Description Generation for Image using Deep Learning Architecture

Natural Language Description Generation for Image using Deep Learning Architecture

Call for Papers

Volume-8 | Issue-6

Last date : 27-Dec-2024

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

First Update : Within 7 Days after submittion

Submit Paper Online

For Author

Research Area


Natural Language Description Generation for Image using Deep Learning Architecture


Phyu Phyu Khaing | Mie Mie Aung | Myint San



Phyu Phyu Khaing | Mie Mie Aung | Myint San "Natural Language Description Generation for Image using Deep Learning Architecture" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5, August 2019, pp.1575-1581, URL: https://www.ijtsrd.com/papers/ijtsrd26708.pdf

Automatic natural description generation of an image is currently a challenging task. To generate a natural language description of the image, the system is implemented by combining with the techniques of computer vision and natural language processing. This paper presents different deep learning models for generating the natural language description of the image. Moreover, we discussed how the deep learning model, which works for the natural language description of an image, can be implemented. This deep learning model consists of Convolutional Neural Network (CNN) as well as Recurrent Neural Network (RNN). The CNN is used for extracting the features from the image and RNN is used for generating the natural language description. To implement the deep learning model in generating the natural language description of an image, we have applied the Flickr 8K dataset and we have also evaluated the performance of the model using the standard evaluation matrices. These experiments show that the model is frequently giving accurate natural language descriptions for an input image.

natural language description, computer vision, natural language processing, deep learning model


IJTSRD26708
Volume-3 | Issue-5, August 2019
1575-1581
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

Thomson Reuters
Google Scholer
Academia.edu

ResearchBib
Scribd.com
archive

PdfSR
issuu
Slideshare

WorldJournalAlerts
Twitter
Linkedin