# Sign Language Interpreting System Using Recursive Neural Networks
## 5 Results and Discussion  
We conducted the first experiment we explained in Section 4. This was an offline setup consisting of training the base Models for different layer sizes, and then we compared the resulting models to determine the best performing model considering its predictions on test data. We found that the metrics of Model 2 are too low and in consequence, we disregarded it whereas Models 3 and 4 already perform high, and thus, we think that no further exploration in this regard was necessary.  
On the basis of the above criteria, we trained the different setups listed in Table 4 and determined from the test that the setup Model 1-A performed higher compared to Models 1-B and C; in consequence, we disregarded Models 1-B to C and in the remainder of this paper, we refer to the Model 1-A as \"Model 1\".  
We explored the influence of adding a Dropout stage, considering Dropout rates of 30%, 40%, 50%, and 60%. The results for the model we identified as the highest performing model for our system are shown in Table 5. We found that adding a Dropout layer to improve performance is ineffective for the particular models we proposed, furthermore, the performance of the resulting models are lower compared to their counterparts that do not include a dropout stage. The corresponding results for the other  
We can compare our system to the one proposed by [PERSON] [14], which reached an accuracy of 95%, in contrast to our system, which achieved an accuracy of 93% in the tests we carried out. Also, the work of [PERSON] [12] exhibited an accuracy of 93.46%; that is, a result similar to those obtained by our system in the tests we conducted.  
Next, we conducted an online test where we evaluated the system's accuracy in processing live video. The setup is illustrated previously in Figure 1. In this case, we are interested in measuring how well the proposed system performs; to achieve this, we measured the AELVT metric we defined in Section 4. The results are reported in Table 6.  
We prioritized the model that exhibits lower AELVT since it might confuse the user of the system. Under this criteria, we consider Model 1 as the best approach among the proposed models.  
A comparison versus the approaches discussed in Section is as follows: the method proposed by [PERSON] [16] achieved a rate of 33.8% of recognition rate whereas we reached an error of 11% through AELVT, thus the performance of the proposed system is higher.  
The Figure 4 illustrates examples of the online tests, here we can see the operation to recognize several words.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Model 1** & **Model 2** & **Model 3** & **Model 4** \\ \hline Precision per class & 0.93333 & 0.03333 & 1.0 & 1.0 \\ \hline Recall per class & 0.93333 & 0.03333 & 1.0 & 1.0 \\ \hline F1 score & 0.93333 & 0.03333 & 1.0 & 1.0 \\ \hline Accuracy mean & 0.93333 & 0.03333 & 1.0 & 1.0 \\ \hline Recall mean & 0.90625 & 0.05882 & 1.0 & 1.0 \\ \hline F1 score mean & 0.89583 & 0.00379 & 1.0 & 1.0 \\ \hline Error rate & 0.06667 & 0.96666 & 0.0 & 0.0 \\ \hline AELVT & 0.11 & 0 & 0.14 & 0.1175 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of metrics for all the proposed models.  
Figure 4: Examples of online operation using live video feeds for different words.