Text to Image using Machine Learning

Year : 2024 | Volume :11 | Issue : 02 | Page : –
By

Aashutosh Meshram,

Yogini Kapse,

Shreedhar Mhatre,

Sandeep Patil,

  1. Student Department of Electronics & Telecommunication, Smt. Kashibai Navale College of Engineering, Pune Maharashtra India
  2. Student Department of Electronics & Telecommunication, Smt. Kashibai Navale College of Engineering, Pune Maharashtra India
  3. Student Department of Electronics & Telecommunication, Smt. Kashibai Navale College of Engineering, Pune Maharashtra India
  4. Assistant Professor Department of Electronics & Telecommunication, Smt. Kashibai Navale College of Engineering, Pune Maharashtra India

Abstract

In the era of digital transformation, our project addresses the convergence of computer vision and natural language processing to enhance user interaction and visual content creation. This project comprises three distinct modules: user authentication and session management, image colorization from grayscale inputs, and text-to-image generation. The login registration module provides secure access to the system, ensuring user privacy and data integrity. Once authenticated, users can utilize advanced computer vision techniques through our image colorization module, where grayscale images are transformed into vibrant, realistic color representations using deep learning models and OpenCV. Additionally, our text-to-image generation module leverages state-of-the-art natural language processing models to convert textual descriptions into visually compelling images. These modules are integrated into a Flask-based web application, ensuring seamless user interaction through intuitive interfaces and robust backend processing. The project not only showcases technical proficiency in AI-driven applications but also underscores practical applications in enhancing digital content creation and user experience.

Keywords: Computer Vision, Natural Language Processing (NLP), Deep Learning, Flask Web Application, Image Colorization, Text-to- Image Generation

[This article belongs to Journal of Image Processing & Pattern Recognition Progress(joipprp)]

How to cite this article: Aashutosh Meshram, Yogini Kapse, Shreedhar Mhatre, Sandeep Patil. Text to Image using Machine Learning. Journal of Image Processing & Pattern Recognition Progress. 2024; 11(02):-.
How to cite this URL: Aashutosh Meshram, Yogini Kapse, Shreedhar Mhatre, Sandeep Patil. Text to Image using Machine Learning. Journal of Image Processing & Pattern Recognition Progress. 2024; 11(02):-. Available from: https://journals.stmjournals.com/joipprp/article=2024/view=159326



References

  1. Real-Time Face Mask Detection using OpenCV and DeepLearning, Department of ECE, KoneruLakshmaiah Education Foundation, Andhra Pradesh, India.
  2. Mishra S, Verma V, Akhtar N, Chaturvedi S, Perwej Y. An intelligent motion detection using OpenCV. International Journal of Scientific Research in Science, Engineering, and Technology. 2022 Mar 5;9(2):51-63.
  3. You Y, Gong S, Liu C. Adaptive moving object detection algorithm based on back ground subtraction and motion estimation. Int. J. Advancements in Computing Technology. 2013;5(6):357-63.
  4. Murshed M, Ramirez A, Chae O. Statistical background modeling: an edge segment based moving object detection approach. In2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance 2010 Aug 29 (pp. 300-306). IEEE.
  5. Duraimurugan N, Chokkalingam SP. Real-Time Object Detection with Yolo. International Journal of Engineering and Advanced Technology (IJEAT). 2019 Feb;8(3S):578-81.
  6. Marengoni M, Stringhini D. High level computer vision using opencv. In2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials 2011 Aug 28 (pp. 11-24). IEEE.
  7. Gürel C, Erden A. FACE DETECTION ALGORTIHM WITH FACIAL FEATURE EXTRACTION FOR FACE RECOGNITION SYSTEM. InThe 20th Int. Conf. on Mechatronics and Machine Vision in Practice 2013 (pp. 1-7).
  8. Huang P, Liu Y, Fu C, Zhao L. Multi-Semantic fusion generative adversarial network for text-to-image generation. In2023 IEEE 8th International Conference on Big Data Analytics (ICBDA) 2023 Mar 3 (pp. 159-164). IEEE.
  9. Raghavan V, Sree SR, Kaladevi R, Hariharan S, Bhanuprasad A. Black and White Image Colorization using Deep Learning. In2022 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) 2022 Dec 22 (Vol. 2, pp. 27-30). IEEE.
  10. Meshram K, Jadhav H, Narsale N, Raut R, Devkar A. Image Generation from Random Noise using Generative Adversarial Networks. In2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) 2023 Apr 28 (pp. 381-385). IEEE.
  11. Xue A. End-to-end chinese landscape painting creation using generative adversarial networks. InProceedings of the IEEE/CVF Winter conference on applications of computer vision 2021 (pp. 3863-3871).
  12. Nimala K, Geetha G, Ponsam JG. Colorization of Black & White Videos & Photographs. In2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) 2022 Jul 15 (pp. 1-6). IEEE.

Regular Issue Subscription Review Article
Volume 11
Issue 02
Received July 3, 2024
Accepted July 20, 2024
Published July 30, 2024