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2. 142 GOVERNMENT POLYTECHNIC COLLEGE
KELAMANGALAM
REVIEW-01
DEPARTMENT OF COMPUTER
ENGINEERING
IMAGE CAPTIONING USING DEEP LEARNING
FOR MULTILANGUAGE
3. PROJECT MEMBERS
SAHANA .S
AMUDHA .G
DHIVYA .B
MONISHA.Y
SOWMIYA .V
VEDHA SHREE .M
- 23501538
- 23501506
- 23501515
- 23501526
- 23501545
- 23501555
PROJECT GUIDE;
B.SANTHIMEENA. M.E.,HOD..
4. ABSTRACT
This project's major goal is to Produce a version that makes it
possible to foresee the caption or text that will appear next to
the image.
The creation of captions for pictures is a difficult and
responsible task.
This image captioning is a complex and challenging tasks.
The main aim of this project is to develop a model such that it
can predict the closest text or caption to that particular image.
5. INTRODUCTION
The term "photo captioning" refers to the explanation of the image's content.
Picture captions are a brand-new trend that is currently generating a lot of
curiosity.
The main objective of this photo capture is to generate a natural language
description for the entry photo that is sent to the version.
In this analysis, we provide a model that depicts characterization as a
linguistic technique using herbs.
Also, the neural network's functionalities are taken from extraordinary
architectures like CNN and LSTM.
6. EXISTING SYSTEM
In the beginning it is impractical to computer to
characterize an image.
There are still some problems in existing systems.
Disadvantages:
Improving the Quality of Captions.
Not Generating creative captions.
Handling unseen objects.
Dealing with multiple objects and relationships are
7. PROPOSED SYSTEM
Image Caption Generator Model (CNN-RNN model)
= CNN + LSTM
A pre-trained model called Xception is used for
this.
CNN – To extract features from the image.
LSTM – To generate a description from the
extracted information of the image.
