# A New Computational Method for Arabic Calligraphy Style Representation and Classification
## 5 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**EDMS/Decision**} & \multirow{2}{*}{**EDM1/NN [17]**} & \multicolumn{2}{c}{**EDM1+LBP/Random**} & \multirow{2}{*}{**Allaf/NN [24]**} & \multicolumn{2}{c}{**Auto-Encoder**} & \multirow{2}{*}{**Our Method**} \\ \multicolumn{2}{c}{**Tree [16]**} & & & & **Forest [20]** & & \\ \hline Diwani & 76\% & 75\% & 90\% & 30\% & 35\% & 98\% \\ \hline Naskh & 92\% & 87\% & 98\% & 81\% & 48\% & 99\% \\ \hline [PERSON] & 56\% & 58\% & 80\% & 23\% & 2\% & 97\% \\ \hline [PERSON] & 54\% & 53\% & 77\% & 45\% & 12\% & 98\% \\ \hline [PERSON] & 63\% & 56\% & 78\% & 73\% & 43\% & 94\% \\ \hline [PERSON] & 66\% & 77\% & 75\% & 46\% & 15\% & 97\% \\ \hline [PERSON] & 64\% & 72\% & 93\% & 73\% & 14\% & 98\% \\ \hline [PERSON] & 41\% & 60\% & 79\% & 45\% & 33\% & 94\% \\ \hline [PERSON] & 96\% & 94\% & 97\% & 94\% & 80\% & 99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: F1-score at style-level yielded by our method and other related works. Figure 11: Recall and precision generated by the proposed method compared to other AC-related works. From Table 5, we can see that our proposed method produces satisfying results with most of the styles. However, Mohakik and Thuluth have misclassified one as the other in some cases, which decreases the accuracy. This is because both styles Mohakik and Thuluth have similar shapes with less unique features that can be used to distinguish one from the other. **Scenario 3:** Compare to other texture and deep learning methods  
In a previous work [15], we proved that AC is better to be dealt with as texture. Since AC text is relatively a homogeneous (i.e., stationary) texture with repeated patterns (characters, diacritics, etc. ), using statistical feature descriptors yields better results than learning-based including deep learning [33]. Furthermore, descriptors that are designed to be used with heterogenous images are harder to train (i.e., more images are needed) than descriptors dedicated to a specific family of images. That is to say, all the descriptors that we compare to our method will poorly perform in cases of a small training set. To prove these claims, an evaluation of statistical against learning-based (deep learning) methods in both normal and small training set size has been carried out. Figure 12 represents precision obtained in both cases. From Figure 12, it appears that our method outperforms all other methods in both 33% and 10% training cases. As we have stated above, statistical descriptors (e.g., BSIF and LPQ) that are designed to be used with heterogeneous textures have performed well with AC style recognition. However, we have claimed that such descriptors lose performance in the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline [PERSON] & 100\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% \\ \hline [PERSON] & 0\% & 100\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% \\ \hline [PERSON] & 2\% & 0\% & 96\% & 2\% & 0\% & 0\% & 0\% & 0\% & 0\% \\ \hline [PERSON] & 0\% & 0\% & 1\% & 98\% & 0\% & 1\% & 0\% & 0\% & 0\% \\ \hline Thuluth & 0\% & 0\% & 1\% & 0\% & 95\% & 0\% & 0\% & 3\% & 1\% \\ \hline Maghribi & 0\% & 0\% & 1\% & 0\% & 1\% & 99\% & 0\% & 0\% & 0\% \\ \hline Kufi & 0\% & 0\% & 0\% & 0\% & 0\% & 2\% & 98\% & 0\% & 0\% \\ \hline Mohakik & 0\% & 0\% & 0\% & 0\% & 5\% & 0\% & 0\% & 94\% & 1\% \\ \hline S-Kufi & 0\% & 0\% & 0\% & 1\% & 0\% & 0\% & 1\% & 0\% & 99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The confusion matrix, of the nine AC styles, generated by our proposed method. Figure 12: Related works evaluated and compared against our method using 33% and 10% images subset respectively for training. case of using a few samples for training. The proof of this claim can easily be noted in the accuracy drop of all methods in the case of using a small image set for training.