**6. Authentication of results**

The comparison between Arabic signature recognition methods were by verification rate and not by the computational time. The accuracy performance measure has been computed using confusion matrix Where, TP signifies the number of true positive signatures, TN refers to the number of true negative signatures, FP signifies the number of false positive cases and FN signifies the number of false negative signatures. True Positive Ratio is the measure of genuine signatures classified correctly as genuine; False Positive Rate is the measure of a forgery signature classified as genuine. False Negative Rate is the measure of a genuine situation classified as forgery. True Negative Proportion is a measure of a forgery signature classified as forgery.

The Verification Rate ¼ ðTP þ TNÞ*=*ðTP þ TN þ FP þ FNÞ � 100%

The tests assumed that 99.8% accuracy proportion Predicted Valu and Decision Tree. Such promising results are pinpointing the state-of-the-art preprocessing techniques and best performance of proposed features to discriminate between genuine and forged signatures with higher accuracy rate. The authentication of the achievements of the suggested method was achieved applying the verification rate using DCT + SPCA method were computed and compared against the two other vastly agreeable autograph verification methods. **Table 3** shows the simulation results with the Arabic signatures consisting 2000 signatures from 100 various signers. The validation rate for the proposed technique is 99.8% attesting to its superiority against the others. We could conclude that DCT + SPCA features technique and Decision Tree classifier was a credible and reliable technique for verification of offline Arabic signatures.


#### **Table 3.**

is generated from 40 different signers using two tasters from each one. As can be realized, the bows represent the number of selected features in the two tasters of the same writer are close to each other for DCT + SPCA method. This seems consistent with the supposition that the value of selected features is a signer-dependent

*The number of selected important features of DCT + SPCA method for the two samples of 40 signers.*

The matching phase is when the model is created using Classification and Regression Tree (Tree) and Support Vector Machines Classification (SVM) with different input parameters. Based on a person's signature, a model was created for the original and forgery signatures. The performance of the proposed method on 100 signers from Arabic signatures were used in identification for classification using DCT + SPCA features for selected important features with SVM classifier achieved the verification rate of 98.7%, and EER of 1.90% and same DCT + SPCA features with Tree classification achieved the verification rate of 99.8%, and the EER dropped to 1.20%. which was better than other techniques, as shown in **Table 2**. The objective of this study was creating a system that 1) can identify handwritten signatures and verify their authenticity, and 2 distinguish forgery from genuine ones, and those created under pressure and other influences. Using 2000 Arabic signatures samples. The results of the matching phase are shown in **Table 2**. This implies that a forger may not skillfully repeat all aspects of the original signature. It also shows a pattern in forgers, which has small variations. Evidence shows that the mean of a feature produced by a forger in multiple attempts at forging tends to lie in a small range. Conversely, genuine signatures produced by a signer may vary under unusual conditions. Signers possess certain unconscious

> **Verification Rate**

Tree+ DCT+SPCA method 99.8% 1.20 98.5% SVM+ DCT+SPCA method 98.7% 1.90 97%

**Verification EER**

**Recognition Rate**

**Classification Techniques with Features Selection**

*Experimental results obtained from 100 signer based on Arabic signatures.*

feature.

**Figure 5.**

*Applications of Pattern Recognition*

**5.4 Matching**

**Technique**

**Table 2.**

**104**

*An assessment table relating between the projected Arabic signature recognition system based on Arabic signatures and other signatures with other previously known approaches.*

### **7. Conclusion**

This paper, we described a method we developed to important features selection using DCT + SPCA features technique in offline Arabic signature verification. It employed the partition of signature samples into 14x14 windows and generated the features extracted for each window. Then, this feature selection was used for classification techniques.

We have mentioned the limitation of the research in the apply of set of Arabic signatures for collecting the Arabic signature samples used in this study. To judge our findings objectively, we used Arabic signatures, which includes Arabic signers. The results of our study show that this method was a credible technique for offline Arabic signature feature selection. This method can be used as a Arabic signature verification method for the exposure of offline signatures.

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In the simulation phase, two different comparisons have been made. The first was the performance of support Vector Machine classifier and DCT+ SPCA features technique, and the second was the performance of Decision Tree classifiers with DCT+ SPCA features technique working together. The Decision Tree classifiers and DCT+ SPCA features technique produced the best verification rate of 99%, which improved the performance of offline Arabic signature verification.

There are many extensions which can be employed to develop the study. The proposed future works can be divided into two main fragments. Firstly, the extension is by an expansion of the procedures with more accuracy for autographs verification. Secondly, the extensions which can be made to the different dataset of autographs.
