AI BASED TEXT TO IMAGE GENERATION USING CNN

Computer Engineering

This paper presents a novel approach to generate images from textual descriptions using Convolutional Neural Networks (CNN). The system interprets user input in natural language and synthesizes a visually coherent image that represents the input text. By leveraging a deep learning-based encoder-decoder architecture, the model converts linguistic features into spatial image representations. The CNN acts as a generative decoder that progressively constructs images from learned text embeddings. This technique finds practical applications in art generation, accessibility tools, content creation, and more. The implementation demonstrates promising results with moderate computational needs and strong generalization for simple to moderately complex image concepts. This technology has broad applications across various domains, including photo-searching, digital art, computer-aided design, and image reconstruction. A key component in this process is the Convolutional Neural Network (CNN), a deep learning architecture renowned for its effectiveness in image recognition and feature extraction. CNNs excel in processing visual data, making them ideal for tasks requiring high accuracy in image classification and generation.

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