Text-to-image generation with enhanced GANs: Bridging semantic gaps using RNN and CNN.

ABSTRACT: Text-to-image generation is the process of generating images from a given text description. It is the most challenging task to produce consistently realistic images according to our conditions. We have considered this problem in our study and proposed a neural network-based model that can generate good-quality images from text descriptions. In this research, we have used a Generative Adversarial Network (GAN) for the generation of images with Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). RNN is used for creating word embeddings from textual sentences and for extracting important features from images we have used CNN. The generator model is used for generating images from text and this generated image is used as input to the discriminator with further matched text,

SUBMITTER: Ramzan S 

PROVIDER: S-EPMC12822966 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature