• Aachal G. Gajbhiye
• Dr. R. R. Keole
• Dr. A. P. Jadhao
• Prof. D. G. Ingale

The proposed text-to-image model delivers high-quality, visually appealing images that closely match input descriptions. By combining Natural Language Processing with CNNs like VGG-16 and ResNet50, and leveraging Generative Adversarial Networks (GANs), the system effectively translates text into accurate visuals. The generator-discriminator architecture of GANs enhances image realism and fidelity. Our experimental results show strong alignment between generated images and textual inputs. This approach offers significant potential for creative industries such as art, advertising, education, and digital content creation, streamlining the process of visual generation from text.

Advanced, Deep Learning, Techniques, Text-to-Image, Synthesis, GANs, NLP Models

"Advanced Deep Learning Techniques for Text-to-Image Synthesis Using GANs and NLP Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.j67-j77, May-2025, Available :http://www.jetir.org/papers/JETIR2505A06.pdf

"Advanced Deep Learning Techniques for Text-to-Image Synthesis Using GANs and NLP Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppj67-j77, May-2025, Available at : http://www.jetir.org/papers/JETIR2505A06.pdf