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Text-to-image synthesis is a fascinating area of research that aims to generate images based on textual descriptions. The main goal of this field is to generate images that match the given textual description in terms of both semantic consistency and image realism. While text-to-image synthesis has shown remarkable progress in recent years, it still faces several challenges, mainly related to the level of image realism and semantic consistency. To address these challenges, various approaches have been proposed, which mainly rely on Generative Adversarial Networks (GANs) for optimal performance. This paper provides a review of the existing text-to-image synthesis approaches, which are categorized into four groups: image realism, multiple scene, semantic enhancement, and style transfer. In addition to discussing the existing approaches, this paper also reviews the widely used datasets for text-to-image synthesis, including Oxford-102, CUB-200-2011, and COCO. The evaluation metrics used in this field are also discussed, including Inception Score, Fr&#x00E9;chet Inception Distance, Structural Similarity Index, R-precision, Visual-Semantic Similarity, and Semantic Object Accuracy. The paper also offers a compilation of the performance of existing works in the field.

Recent Advances in Text-to-Image Synthesis: Approaches, Datasets and Future Research Prospects