# A New Computational Method for Arabic Calligraphy Style Representation and Classification
## 5 Experimental Results and Discussion  
This section is dedicated to evaluating the performance of the proposed solution. Three scenarios have been carried out; the first one is meant to evaluate each descriptor individually to validate the role it has been proposed for in the first place. In the second scenario, the decisions yielded by all classifiers are combined to produce a final decision. We devote this final scenario to examine the effect of using few samples for training. All the experiments have been carried out under the following configuration:  
* **Dataset:** We use the same dataset adopted in our previous works [15; 27]. It consists of nine (9) classes each of which represent an Arabic style. The total number of the dataset's images is 1685. Each style has several images that vary from 180 to 195. * **Classification:** For image classification, we use the well-known classifier Support Vector Machine (SVM). Support Vector Machine classifier is a supervised machine learning technique. The main goal of this supervised method is to find a function in a multidimensional space that is able to separate training data with known class labels. SVM can be used for linear as well as nonlinear data classification. In our case, we use the Polynomial Kernel. This choice is a result of the outperformance SVM showed compared to other classifiers. To avoid overfitting, 3-fold cross-validation has been adopted. * **Metrics:** To validate our model, we use four metrics, namely: Recall (R), Precision (P), F1-score, and Accuracy given by the following formulas. \[R=\frac{treue\ positif}{true\ positif+false\ negatif}\] \[P=\frac{true\ positif}{true\ positif+false\ positif}\] \[F1-score=2.\frac{R\cdot P}{R+P}\] \[Accuracy=\frac{true\ positif+true\ negatif}{true\ positif+false\ positif+true\ negatif+false\ negatif}\] **Scenario 1. ** Experimenting with each descriptor individually:  
Figure 10: A slanted word from Diwani style, with its orientation measured, detected using the flood-fill algorithm. As we discussed beforehand, some descriptors have been mainly proposed to identify one single style, whereas some others are general and can be used to separate multiple styles. This scenario is dedicated to analyzing the results for each descriptor individually. It should be mentioned that each descriptor has been used to train a separated SVM. Table 3 presents the accuracies yielded by each feature descriptor for each style. From Table 3, we can see that the descriptors that have been proposed for one or two specific styles have performed adequately. HVSL and WOr as instances generated accuracies of 100% and 85% respectively, which are the highest ones compared to others. However, LVL yielded a similar performance for both Kufi and Square-Kufi, although it has been proposed for the former. This could be attributed to the similarity between these two styles. The Tth descriptor works well for several styles that are characterized by their thickness. Those styles are Naskh, Thuluth, Kufi, and Square-Kufi. The highest accuracy was for Square-Kufi with a correct rate of 82%. In contrast, the lowest accuracy was for Mohakik style with 43%. HPP descriptor has worked well with most of the styles because it is a representation of a general characteristic. The lowest accuracy, which is 58%, produced with the style Farisi because it can be written in different manners (some texts are written on the baseline; in some others, words are written one above another). We can conclude that general descriptors have the highest accuracies. Nevertheless, by combining these general descriptors with the specific ones, even better results can be achieved. In the following scenario, we evaluate the impact of combining all the descriptors and compare the results to other proposed methods including deep learning. **Scenario 2**: combine the descriptors  
After having proved the efficiency of the individual descriptors, we combine them to get a powerful descriptor that can be used to distinguish the nine AC styles effectively.