Towards Unified Multimodal Search and Generation System: An AI-Driven Text–Image Framework for Arabic

Recent advances in multimodal learning have significantly enhanced natural language understanding (NLU) and information retrieval across various languages; however, Arabic remains substantially underrepresented in this domain. In this work, we propose an innovative deep learning framework that draws inspiration from the Unified Multimodal Search and Generation System paradigm, combined with OpenAI’s CLIP and DALL⋅E architectures, to enhance Arabic-language image retrieval and generation. Given the highly inflected nature of Arabic and the limited application of modern deep learning methods in Arabic NLP, we fine-tune these models on an Arabic dataset to enable effective text–image alignment as well as the generation of semantically coherent images from Arabic queries. Our findings demonstrate the potential of the proposed framework to advance Arabic multimodal processing and contribute to narrowing the gap between Arabic NLP and recent developments in multimodal learning.