GENERATING SYNTHETIC IMAGES FROM TEXT USING RNN & CNN

Abstract

Generating synthetic images from textual descriptions presents a challenging yet 
promising avenue in the field of computer vision and natural language processing. 
This study proposes a novel approach that combines Recurrent Neural Networks 
(RNNs) and Convolutional Neural Networks (CNNs) to generate realistic images 
based on textual input. The RNN component processes the textual descriptions, 
capturing semantic information and contextual dependencies, while the CNN 
component generates corresponding image features. These features are then fused to 
produce high-quality synthetic images that closely match the provided textual 
descriptions. The proposed method leverages the strengths of both RNNs and CNNs, 
enabling effective modeling of complex relationships between textual and visual data. 
Through extensive experimentation and evaluation on benchmark datasets, the 
proposed approach demonstrates superior performance in generating diverse and 
visually plausible images compared to existing methods. This research opens up new 
possibilities for applications such as image synthesis from textual prompts, creative 
content generation, and data augmentation in computer vision tasks.