## 7 Discussion  
The primary goal of this survey is to analyze the outcomes from studies on Arabic and English image captioning. We conducted a systematic approach to collect papers related to encoder-decoder image captioning. The article collection process is shown in detail in Section 2. Based on selection criteria, we have selected 52 papers, and the techniques used in these papers are explained in Sections 3, 4, 5, and 6, respectively. Specifically, we mentioned the main five components used to build the IC models: the language models, feature extraction model, loss functions, available datasets, and evaluation metrics. These findings require more analysis, which this section seeks to accomplish by answering the survey questions in Table 2. The answers to the survey questions are listed below. **RQ1. How do image caption generation techniques identify the important objects in the image? ** The image caption pipeline's initial challenge is to represent visual material adequately. Two categories may be used to categorize the existing visual coding techniques: object detection and CNN features. Each category falls under two other categories, explained in Section 3. The object detection category, which mostly depends on Faster R-CNN, \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Dataset** & **Access** & **Size** & **Notes** \\ \hline \hline
[11] & ImageNet dataset & Private & 10,000 images & The captions are manually written in Arabic by professional Arabic translators. \\ \hline
[11] & Al-Jazeera news website & Private & 100,000 images & A native Arabic website. \\ \hline
[12] & Flicker8k dataset & Private & 8000 images & Manually written captions in Arabic by professional Arabic translators. \\ \hline
[12] & Middle Eastern countries’ newspapers & Private & 405,000 images & Arabic \\ \hline
[13] & MSCOCO & Private & 1166 images & Human-generated captions using Crowd-Flower Crowdsourcing [99]. \\ \hline
[13] & Flicker8k & Private & 150 images & Translated by professional translator. \\ \hline
[13] & Flicker8k & Private & 2111 images & Translated by GCT, then verified by Arabic native speakers. \\ \hline
[14] & Flicker8k & Private & 2000 images & Translated using smart translator Ultra edit [100]. \\ \hline
[15] & Flicker8k & Public & 8000 images & Translated using GCT and then validated by a professional Arabic translator. \\ \hline
[16] & ArabicFlickr1K & Private & 1095 images & Framework for creating an image captioning corpus based on active learning. \\ \hline
[17] & MSCOCO & Private & 123,287 images & GCT \\ \hline
[17] & Flicker8k & Private & 8000 images & GCT \\ \hline
[19] & MS COCO & Private & 120,000 images & The translation uses a combination of GTC, Facebook Machine Translation (FMT), and translation services offered by the University of Helsinki (UH). \\ \hline
[19] & Flicker30k & Private & 32,000 images & The translation uses a combination of GTC, FMT, and UH. \\ \hline
[19] & Flicker8k & Private & 8000 images & The translation uses a combination of GTC, FMT, and UH. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of Arabic datasets. includes region-based and graph-based. Global CNN features and grid-based features fall under the CNN Feature category. Region-based features, which followed the development of global and grid features, have been the state-of-the-art option, and most studies used them in image captioning for years because of their appealing performance, as shown in Table 8. However, several new factors have reopened the discussion on the ideal feature model for image captioning. The appearance of Adaptive Attention [76] led to improvement, as we can see in Table 8, although [PERSON] et al. [64] use two types of image features [76], which are superior to them, despite only using grid-based. Also, using complementary data from multiple CNN encoders [49] led to a remarkable development in the field compared to [50].