Text to Image Generation Using Machine Learning

Abstract

A method called text-to-image involves creating images automatically from provided written descriptions. It contributes significantly to artificial intelligence by tackling the problem of integrating textual and visual input. One of the usefulness of automatic picture synthesis is the generation of images using conditional generative models. For this, Generative Adversarial Networks (GANs) are frequently employed. Using GANs, recent developments in the sector have made significant progress. An outstanding illustration of deep learning's potential is the transformation of text into images. It is difficult to create a text-to-image synthesis system that
consistently creates realistic graphics based on predetermined criteria. Many of the existing algorithms in this field struggle to produce visuals that precisely match the given text. In order to solve this issue, we carried out a research work where we concentrated on developing the generative adversarial network (GAN), a deep learning-based architecture. The aim of this research work is to create a system that allows you to generate images that are semantically consistent.