## 4 Experimental Dataset and Simulation Results
### Feature Extraction and Classification  
To recognize the sign word gesture, we evaluated different signs of isolated hand gestures. The proposed CNN with feature fusion method was trained on the entire dataset. In this case, YCbCr and SkinMask segmented images were used as the inputs for the two channels of the proposed CNN model. We used a constructed dataset containing twenty classes, with 18,000 hand gesture images. We used 70% of the images for training purposes and 30% for testing. The features were extracted and fused at the second level of the fully-connected layer. A multi-class SVM classifier was used to classify the different hand gestures and interpret them as text. The average accuracy of the sign word recognition was about 97.28%. This system achieved the highest accuracy of the sign gestures on the \"call\", \"correct\", and \"fine\" gestures, respectively. Some sign gestures were apparently very similar in shape, leading to the misrecognition, which was then recognized by pose changes. For example, \"OK\" and \"help\" and \"cold\" and \"work\" are shown in Figure 12. The confusion matrix of Figure 13 shows the recognition accuracy of our model. In terms of accuracy, input feature sets were also fed with the softmax classification for performance comparison. Figure 14 shows the accuracy of the two classifications of softmax and SVM. Table 1 represents the accuracy of the comparison with state-of-the-art algorithms. As a comparison, we implemented different state-of-the-art algorithms under the same dataset, and the recognition accuracy is shown in Figure 15. The average recognition accuracy using the YCbCr + CNN [24] and SkinMask + CNN [25] methods was 96.58% and 96.11%, respectively. From the results, it can be said that the experimental evaluation of our proposed method improved the recognition accuracy. However, using the SVM classifier, our method achieved 97.28% accuracy, which was higher than the softmax classification. Furthermore, the considered databases were classified using the proposed model. The average classification accuracy is shown in Figure 16.  
Figure 11: Sample images of hand gestures in the benchmark dataset, YCbCr image, and SkinMask segmented images (first row, second row, and third row, respectively).  
Figure 10: Example of the SkinMask segmented images from the datasets.  
The main interface of sign word recognition is shown in Figure 17. The users were requested to perform hand gestures in the ROI location. The system recognized each gesture and interpreted it as text. Figure 18 represents a user simulation of real-time sign word recognition. The system evaluated each gesture during processing, which involved image pre-processing, feature extraction, and sign recognition. The total time to recognize each gesture was 26.03 ms, which was sufficient for real-time recognition at 30 fps. However, the frame rate was limited by the webcam to 33.33 ms per frame. We evaluated the average recognition accuracy in real-time of fifteen participants (mean age 22, male 13, and female 2) who performed five tasks (one task had twenty sign gestures). Figure 19 shows the average accuracy of hand gesture performances for all participants.  
Figure 16: Recognition accuracy of static hand postures using benchmark dataset.  
Figure 14: Performance evaluation of the two classifications, softmax and SVM.  
Figure 15: Comparison of average recognition of hand gestures.  
Figure 19: Average accuracy of the gesture recognition of all participants.  
Figure 17: Main interface of the proposed system.  
Figure 18: Simulation of the sign word recognition system.