Synthetic Text Image

Synthetic text image generation leverages advancements in large language models and text-to-image models to create artificial datasets for training computer vision models, addressing the limitations of expensive and time-consuming manual annotation of real-world images. Current research focuses on improving the realism and diversity of synthetic images, often employing diffusion models and convolutional neural networks, and exploring techniques to bridge the domain gap between synthetic and real data for improved downstream task performance. This work has significant implications for various applications, including disaster assessment, visual-language model training, and scene text detection, by enabling the creation of large-scale, high-quality training datasets where real data is scarce or expensive to acquire.