## 6 Arabic Image Captioning (AIC)
### Language Models  
Language models are used like those used with English. Language models such as RNN, LSTM, GRU, and Transformer can handle Arabic. The first research in the Arabic language [11] used the Deep Belief Network as a language model. A year later, the authors updated the language model While maintaining the same model architecture in [12] by replacing the deep belief network with LSTM. The experimental results showed promising performance. [PERSON] et al. [13] and [PERSON] et al. [15] use a single-layer LSTM with 256 memory units, while [PERSON] et al. [19] use two-layer LSTM with 256 memory units. Also, many other research works use LSTM, such as [14,16,17,21]. [PERSON] et al. [18] compare LSTM and GRU, but they do not notice an appreciable difference when using LSTM instead of GRU. [PERSON] et al. [20] initialize pre-trained transformers on different Arabic corpora as a language model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Study** & **Architecture** & **Attention** \\ \hline
2017 & [11] & Compositional & No \\
2018 & [12] & Compositional & No \\
2018 & [13] & Encoder-decoder & No \\
2018 & [14] & Encoder-decoder & No \\
2020 & [15] & Encoder-decoder & No \\
2020 & [16] & Encoder-decoder & No \\
2021 & [17] & Encoder-decoder & Yes \\
2021 & [18] & Encoder-decoder & No \\
2022 & [19] & Encoder-decoder & Yes \\
2022 & [20] & Encoder-decoder & Yes \\
2022 & [21] & Encoder-decoder & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 6: Distribution of the architectures and Attention used in the studies.  
**Text Preprocessing.** Arabic is distinct from other languages because of its ambiguous, complicated structure and diversity of dialects, which the computational system must take into account at every linguistic level. Due to its complexity in form and meaning, Arabic verb morphology is essential to creating an Arabic sentence [94]. Therefore, it is necessary to preprocess the Arabic text to simplify it somewhat so the model can handle it.  
[PERSON] [11; 12] uses root words based on caption generation in Arabic for images. He first creates image fragments using a previously trained deep neural network on ImageNet. After that, these fragments are mapped to a set of root words in Arabic. A deep belief network chooses different root words associated with the image fragments and extracts the most relevant words. Finally, he uses dependency tree relations to construct a sentence from the words [11]. [PERSON] et al. [15] followed the Arabic preprocessing methods suggested by [95]. The word ending characters 't marbouta' and 'ya' maqsoura' are normalized, the 'hamza' on characters is normalized, and diacritics are eliminated. Additionally, they eliminate punctuation and non-Arabic letters. [PERSON] et al. [18] followed four preprocessing methods to test their effect on the final result. In the first method, they use the caption without preprocessing. In the second method, they followed the method in [95]. In the third method, they remove the alir tanseen. In the fourth method, they keep the single letter away. It was found that the fourth method, which integrates the various preprocessing steps without deleting the waw, was the best. Hence, the importance of preprocessing the Arabic text becomes clear to us. [PERSON] et al. [21] tokenize the Arabic text using FARASA word segmenter and Pyarabic [96], which splits captions into tokens based on spaces. FARASA [97] separates Arabic words into their component prefixes, stems, and suffixes.