What is it about?

This paper looks at how modern artificial intelligence systems can automatically describe images using natural, human-like language, especially in more than one language. It explains how recent AI models learn to focus on important parts of an image, such as people, objects, and actions, to generate clearer and more meaningful descriptions. The paper also compares existing approaches, discusses the types of image datasets used for training, and highlights current challenges, such as limited support for many languages. The work helps readers understand where image captioning technology stands today and how it can be improved for real-world use in areas like accessibility, education, and global communication.

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Why is it important?

This article is important and timely because images are being shared online at an unprecedented rate, yet much of this visual content remains inaccessible to Arabic-speaking users and to technologies that rely on textual information. While recent advances in artificial intelligence have significantly improved image description in English, similar progress for Arabic has been limited due to the language’s complexity and lack of dedicated resources. This work addresses that gap by focusing specifically on Arabic image captioning and by demonstrating how modern attention-based models can produce more accurate and meaningful descriptions. The findings help move image captioning beyond English-centric solutions and support more inclusive, multilingual AI systems that can benefit education, accessibility tools for visually impaired users, digital media, and future AI applications across the Arabic-speaking world.

Perspectives

This article was developed to explore how modern attention-based artificial intelligence models can be used to automatically generate meaningful descriptions for images in the Arabic language. The work was driven by the growing need to make visual content more accessible and inclusive for Arabic-speaking communities, where technological support has lagged behind that of English-focused systems. By addressing linguistic complexity and data limitations, the study contributes toward more equitable and practical image understanding technologies and is intended to support future research and real-world applications in accessibility, education, and digital media.

Dr Omar S Al-Kadi
University of Jordan

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This page is a summary of: Attention-based transformer model for Arabic image captioning, Neural Computing and Applications, May 2025, Springer Science + Business Media,
DOI: 10.1007/s00521-025-11199-1.
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