A Systematic Literature Review on Using the Encoder-Decoder Models for Image Captioning in English and Arabic Languages

Abstract

With the explosion of visual content on the Internet, creating captions for images has become a necessary task and an exciting topic for many researchers. Furthermore, image captioning is becoming increasingly important as the number of people utilizing social media platforms grows. While there is extensive research on English image captioning (EIC), studies focusing on image captioning in other languages, especially Arabic, are limited. There has also yet to be an attempt to survey Arabic image captioning (AIC) systematically. This research aims to systematically survey encoder-decoder EIC while considering the following aspects: visual model, language model, loss functions, datasets, evaluation metrics, model comparison, and adaptability to the Arabic language. A systematic review of the literature on EIC and AIC approaches published in the past nine years (2015–2023) from well-known databases (Google Scholar, ScienceDirect, IEEE Xplore) is undertaken. We have identified 52 primary English and Arabic studies relevant to our objectives (The number of articles on Arabic captioning is 11, and the rest are for the English language). The literature review shows that applying the English-specific models to the Arabic language is possible, with the use of a high-quality Arabic database and following the appropriate preprocessing. Moreover, we discuss some limitations and ideas to solve them as a future direction.