# A New Computational Method for Arabic Calligraphy Style Representation and Classification
## 5 Experimental Results and Discussion
To this end, each descriptor is used to train a separated classifier (SVM in our case) and the overall decision will be the sum of the decisions generated by all the classifiers. In other words, the final decision will be the one with the highest votes. Figure 11 represents the precision and recall yielded by our combined descriptor compared to other methods from state of the art [16; 17; 20; 24; 25]. From Figure 11, it seems that our proposed method outperforms all other related works with a precision of 97%. This remarkable performance of ours is a result of combining all AC features used by calligraphers to recognize different styles in one descriptor. The method proposed in [20] (EDM1+LBP) has scored the second-best performance. This is because the latter deals with AC images as texture that is an approach; we prove its effectiveness in [22]. Although the work [23] has opted for morphological descriptors as we did, the results were too weak. This is because the authors have taken into account the general features only and did not consider the specificity of each AC style. On the other \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **[PERSON]** & **Naskh** & **Farisi** & **Rekaa** & **Thuluth** & **Maghribi** & **Kufi** & **Mohakik** & **Square-Kufi** \\ \hline HVSL (Square-Kufi) & 63\% & 62\% & 34\% & 37\% & 83\% & 62\% & 87\% & 74\% & 100\% \\ \hline ToE (General) & 93\% & 88\% & 94\% & 87\% & 88\% & 81\% & 94\% & 86\% & 99\% \\ \hline ToG (General) & 96\% & 88\% & 91\% & 83\% & 90\% & 80\% & 91\% & 86\% & 98\% \\ \hline LVL (Kufi) & 54\% & 77\% & 28\% & 44\% & 49\% & 34\% & 89\% & 25\% & 91\% \\ \hline Tth (General) & 47\% & 61\% & 51\% & 52\% & 66\% & 51\% & 76\% & 43\% & 82\% \\ \hline SDs (Thuluth + Mohakik) & 13\% & 2\% & 0\% & 10\% & 86\% & 11\% & 17\% & 62\% & 18\% \\ \hline WOr (Diwani) & 85\% & 24\% & 9\% & 21\% & 9\% & 6\% & 8\% & 18\% & 62\% \\ \hline HPP (General) & 76\% & 97\% & 58\% & 76\% & 83\% & 92\% & 97\% & 78\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracies yielded by each descriptor. The first column holds descriptors with the respective style they have been proposed for. Horizontal and Vertical Straight Lines (HVSL), Text orientation from Edges (ToE), Text orientation from Skeleton (ToS), Long Vertical Lines (LVL), Text thickness (Tth), Special Diacritics (SDs), Word’s Orientation (WOr), Horizontal Profile Projection (HPP). hand, stacked auto-encoder has scored the worst precision (resp. recall) among all. This is because using stacked auto-encoders needs tens of thousands of images to tune thousands of parameters that constitute the network, especially since the input of the network is the pixels of the image itself rather than a descriptor. Yet, no AC dataset contains this huge number of images. To further evaluate the performance of these methods, F1-score at the style-level has been estimated and listed in Table 4.  
At first glance, Table 4 shows that our proposed method outperforms other methods in all styles. S-kufi seems to be the most distinguishable style among the others due to its unique features. In contrast, Thuluth, Mohakik, and Rakaa seem to be harder to distinguish. This can be attributed to the common way of writing of Rakaa and the similarity between Mohakik and Thuluth. Nevertheless, our proposed method has yielded satisfying results because we took advantage of slight changes among styles such as the unique way of writing diacritics. With most of the styles, our proposed method has produced more than 97% accuracy. However, with Thuluth and Mohakik, our method yields the lowest accuracy, which is 94%. To get an idea about how the styles Thuluth and Mohakik are misclassified, we generate and present the confusion matrix in Table 5.