Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Science, Engineering and Technology Print ISSN: 2395-1990 | Online ISSN : 2394-4099 (www.ijsrset.com) doi : https://doi.org/10.32628/IJSRSET 623 Deep Learning Based Text to Image Generation G. Ajay *1 , Ch. Sai Teja 2 , P. Baswaraj 3 , V. Vasanth 4 , Dr. G. Sreenivasulu 5 *1-4 B.Tech. Student, 5 Professor CSE Department, JB Institute of Engineering and Technology, Hyderabad, India A R T I C L E I N F O A B S T R A C T Article History: Accepted: 05 April 2023 Published: 23 April 2023 Text-to-image generation is a method used for generating images related to given textual descriptions. It has a significant influence on many research areas as well as a diverse set of applications (e.g., photo-searching, photo- editing, art generation, computer-aided design, image re-construction, captioning, and portrait drawing). The most challenging task is to consistently produce realistic images according to given conditions. Existing algorithms for text-to-image generation create pictures that do not properly match the text. We considered this issue in our study and built a deep learning-based architecture for semantically consistent image generation: recurrent convolutional generative adversarial network (RC-GAN). RC- GAN successfully bridges the advancements in text and picture modelling, converting visual notions from words to pixels. The proposed model was trained on the Oxford-102 flowers dataset, and its performance was evaluated using an inception score and PSNR. The experimental results demonstrate that our model is capable of generating more realistic photos of flowers from given captions, with an inception score of 4.15 and a PSNR value of 30.12 dB, respectively. Generating images from natural language is one of the primary applications of conditional generative models. This project uses Generative Adversarial Networks (GANs) to generate an image given a text description. GANs are Deep Neural Networks that are generative models of data. Given a group of coaching data, GANs can learn to estimate the underlying probability distribution of the info. In this project, the model is trained on the Caltech birds dataset. Recent progress has been made using GANs. Keywords: PSNR, GAN, Caltech birds dataset, NLP, CNN, RNN, CNN Publication Issue Volume 10, Issue 2 March-April-2023 Page Number 623-628 I. INTRODUCTION When people listen to or read a narrative, they quickly create pictures in their mind to visualize the content. Many cognitive functions, such as memorization, reasoning ability, and thinking, rely on visual mental imaging or “seeing with the mind’s eye”. Developing a technology that recognizes the connection between vision and words and can produce pictures that represent the meaning of written descriptions is a big step toward user intellectual ability.Image- processing techniques and applications of computer vision (CV)