## 3 Results
This distribution highlights the dominant role of Chinese researchers in this field and the diverse, albeit smaller, contributions from other countries. To present a structured synthesis of the reviewed literature, we divided the studies into three distinct application areas and organized them accordingly into Tables 3-5. Figure 3: Distribution of articles by publisher, showing the percentage contribution of each source included in this review. Figure 2: (**a**) Distribution of research sources highlighting the dominance of journal publications, followed by conference papers or proceedings and theses. (**b**) Publication trends over time showing a significant increase in research activity after 2020. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Paper** & **Year** & **Study Focus** & **Technique** & **Ne Images** & **Dataset Type** & **Dataset Source** & **Fabric/Fiber Type** \\ \hline
[10] & 2021 & Textile material recognition & NIR & 253 & fabric (section) & created & Cotton, polyester, viscose, and blended \\
[13] & 2024 & Garments type classification & CNN & 26,833 & garment (full) & created & 11 garment categories, e.g., shirt, \\
[14] & 2021 & Recycled clothing classification & Alex Net & 3300 & garment (full) & existing & 9 garments categories \\
[15] & 2023 & Automated color sorting of & Object detection and & 277 & garment (full) & created & Not given \\  & multi-colored waste textiles & decision tree & & & & \\
[16] & 2016 & Textile material classification & SIMCA, SVM, and ELM & 120 & fabric (section) & created & Natural and synthetic \\
[17] & 2020 & Natural vs. synthetic fiber identification & PCA, CVA, and k-NN & 350 & fabric (section) & created & Cotton, linear, wool, silk, viscose, \\
[18] & 2021 & Textile material recognition & Neural network & 892 & fabric (swatch) & created & polycarate, and synthetic fiber \\
[19] & 2022 & Identification of textile fibers & CNN vs. (RNN) & & & & \\  & and SVM) & & & & & & \\  & & Supervised & & & & & \\
[20] & 2023 & Classification of fabric material & classification & 104 & multiple & created & Cotton, polyester, wool, silk, and viscose. \\
[21] & 2024 & Fabric identification & Deep learning & 840 & multiple & created & 14 types of fabric \\
[22] & 2023 & Multi-classification of textile waste & Hybrid CNN-LSTM & 10,000 & fabric (section) & mixed & Not given \\
[23] & 2020 & Classification of textile waste & CNNs & 263 & fabric (swatch) & created & Polycarate, wool, cotton, nylon, and blended \\
[24] & 2022 & Classification of fabric material & CNNs & 370 & fabric (unspecified) & created & Cotton, linear, wool, silk, polyester, \\
[25] & 2023 & Identification of textile materials & AONet & 7000 & fabric (microscopic) & existing & Cotton, demim, line, nylon, and silk \\
[26] & 2022 & Textiles material Recognition & CNN & 2764 & fabric (draped) & created & Polycarate, cotton, wool, silk, viscose, \\
[27] & 2023 & Cotton percentage prediction in fabric & CNN & 7000 & fabric (swatch) & existing & Fabric with cotton percentage in it \\
[28] & 2022 & Color classification of waste textiles & CV & 2,100,466 & fabric (draped) & created & Not given \\ \hline \end{tabular}
\end{table} 
Table 3: Overview of studies utilizing AI techniques for textile sorting, highlighting the year, study focus, applied techniques, number of images, dataset type, and image source.