This project introduces an image captioning system that incorporates sound to generate more comprehensive captions. Two extensively trained models are combined - one for computer vision and one for natural language processing. Sound recommendations are made based on the image scene. The system achieves a top 5 accuracy of 67% and top 1 accuracy of 53%, setting a new standard. It is the first image captioning system to offer this level of accuracy while also incorporating audio. This has significant implications for helping visually impaired individuals understand visual content more vividly.

1. MANAKULA VINAYAGAR INSTITUTE OF
TECHNOLOGY
Accredited by NBA & NAAC ‘A’ Grade
MINIPROJECT LAB - ITP63
IMAGE CAPTIONING SYSTEM
TEAM MEMBERS
Sriranjaani. G
Pavithra. K
Sangeetha. R
Subathradevi. P

2. ABSTRACT
Our project introduces a novel approach to image captioning by
incorporating sound and context.
- Two extensively trained models are combined to achieve improved results.
- Sound recommendations are made based on the image scene, enhancing the
overall user experience.
- Captions are generated using a combination of natural language processing
and state-of-the-art computer vision models.
- Achieved Top 5 accuracy of 67% and Top 1 accuracy of 53%, setting a new
standard in image captioning.
- Our model is the first of its kind to offer this level of accuracy and
innovation.
- This approach has significant implications for visually impaired individuals,
providing them with a comprehensive and vivid description of the visual
content.
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3. INTRODUCTION
Image captioning generates descriptive and contextually relevant captions for
images. It combines computer vision and natural language processing
(NLP).The goal of our project is to bridge the gap between visual and textual
information. It utilizes deep learning techniques and advanced neural network
architectures. The CNNs extract visual features, while RNNs or transformers
generate captions. Advancements in machine learning and large datasets have
driven progress. Challenges include capturing fine-grained details, handling
complex scenes, and addressing biases. Techniques like attention mechanisms,
reinforcement learning, and multimodal learning are being explored. Image
captioning finds applications in image understanding, assistive technologies,
content retrieval, and social media. The existing image captioning systems
generate textual captions for images using deep learning models, combining
computer vision and NLP techniques. The proposed system aims to enhance
image captioning by incorporating audio information, allowing for more
comprehensive and contextually rich captions that capture both visual and
auditory aspects of the scene.
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4. Existing Diagram

5. Proposed Diagram

6. Hardware Requirements
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• Processor Intel i5 Processor or higher
• RAM 8GB (or) higher
• Hard disk 256SSD
• System Requirements Laptop (or) PC

7. Software Requirements
• Deep Learning Frameworks
• Speech Recognition Libraries
• Image Processing Libraries
• Natural Language Processing Libraries
• Streamlit frameworks
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8. UML Diagram
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• USE CASE DIAGRAM
• CLASS DIAGRAM
• SEQUENCE DIAGRAM
• STATE DIAGRAM
• ACTIVITY DIAGRAM
• COMPOUND DIAGRAM

9. Use Case Diagram

10. Class Diagram

11. Sequence Diagram

12. State Diagram

13. Activity Diagram

14. Compound Diagram

15. ADVANTAGES
› Enhanced understanding
› Accessibility
› Searchability
› Content indexing and retrieval
› Natural language communication
› Multilingualism
› Educational and training purposes
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16. Input

17. Output Screen

18. conclusion
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Our project represents an innovation in the field,
leveraging both visual and auditory information to
generate more comprehensive and contextually rich
captions. By combining deep learning techniques,
including neural network architectures and audio
processing, we have enhanced the average accuracy of
67%, contextuality, and accessibility of image captions.
Our solution addresses challenges in capturing,
understanding of images. With applications ranging from
assisting visually impaired individuals to improving
image search and retrieval, we are proud to offer a
versatile and impactful tool that enhances content
understanding, fosters inclusive human-computer
interaction, and opens up new possibilities for leveraging
the power of visual and audio information.

19. Future Works
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In our project we enhanced an image with audio
generation. It can be improved by using automation in
image as well as video. It will be helpful in many
multimedia platforms. So, for in this project, the text
description is in English, it can be developed as a
multilanguage description in order to overcome the
language issues. The integration of audio in image
captioning can also improve the accuracy and richness of
the generated captions, as the audio information can
complement and supplement the visual cues captured in
the image.

20. THANK YOU