# A New Computational Method for Arabic Calligraphy Style Representation and Classification
## 3 Related Work  
Arabic text font recognition has been extensively studied owing to the availability of a huge amount of resources that help researchers build new solutions. Works on Arabic font recognition can be grouped into two categories, namely handcrafted- and deep learning-based categories. On the one hand, starting with handcrafted-based solutions, [PERSON] [10] uses discrete curvelet transform (DCT) to convert images into descriptors, and then, a backpropagation neural network has been employed to predict the style of the image text based on the extracted descriptor. In [11, 12], the former method has been reinforced against scale changes by using a steerable pyramid texture. [PERSON] [5] has experimented and compared several texture descriptors for Arabic font recognition using extreme learning machine (ELM) and fast learning machine (FLN). On the other hand, deep convolutional neural networks (DCNN) [13, 14] have been utilized in several occasions to perform Arabic font and font size recognition. While there has been much research on Arabic font, few researchers have dealt with Arabic calligraphy. It could be attributed to the lack of resources such as public datasets and insufficiently related works. However, the reader should know that dealing with calligraphy is much harder than fonts due to the absence of creativity in the latter. In other words, calligraphy texts are affected by the calligrapher's impression, which is not the case with texts generated using machine fonts. Therefore, we limit ourselves to works that tackle the issue of recognizing calligraphy styles rather than fonts. Table 2 lists works conducted on AC style recognition with their respective datasets and the number of styles taken into account. In [15], the authors argued that Arabic calligraphy images should be dealt with as textures, and therefore, they evaluated a set of commonly used texture descriptors for the task of AC identification. Results indicated that Binarized Statistical Image Features (BSIF) is the best texture descriptor among all for such a task. Due to the lack of big datasets of AC and the fine-grained styles (e.g., Mohakik and Thuluth), most works in the literature have utilized classical machine learning tools instead of deep learning. Moreover, they attempted to propose their descriptors for this issue instead of using other common descriptors such as Sift, LBP, etc. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AC Style** & **Example Image** & **General Characteristics** & **Cues Used by Experts** & **Style Source** \\ \hline \multirow{3}{*}{Mohakik} & \multirow{3}{*}{Mohakik and Thuluth have almost the same} & - special orientation under the baseline & \multirow{3}{*}{-Thuluth} \\  & & characteristics. However, letters in Mohakik & & \\  & & are longer under the baseline. & & \\ \cline{1-1}  & & & - written on the baseline & \\ \hline \multirow{3}{*}{Square-Kufi} & \multirow{3}{*}{Mohakik and Thuluth have almost the same} & \multirow{3}{*}{Mohakik and Thuluth have almost the same} & - special orientation under the baseline & \multirow{3}{*}{-Tuluth} \\  & & characteristics. However, letters in Mohakik & & \\ \cline{1-1}  & & are longer under the baseline. & & \\ \cline{1-1}  & & & - written on the baseline & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._In all of their works, [PERSON] [16; 17; 18; 19] and [PERSON] [20] attempt to classify AC styles with a proposed statistical descriptor, named Edge Direction Matrix (EDMs). They firstly pass through two steps called EDM1 and EDM2, which produce 3 \(\times\) 3 matrices counting edges (i.e., pixels' adjacency), and then extract 22 statistical moments to constitute the final image descriptor. Unlike the aforementioned global approach, the works in [21; 22; 23] focused on extracting features using local shapes of letters.