# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 4 Results and Discussion  
In this section, we will look at the results of our research, discussing the findings from our comparative analysis to find the most effective machine learning models for NSL recognition. All the models are evaluated based on accuracy, training time, and prediction time shown in Table 8. We will also take the time to discuss the different use cases and limitations for each model to offer insight into their suitability for NSL recognition. For consistency in our comparative analysis, all the models were evaluated on the same dataset size with a training and test split of 70/30%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sign** & **Precision** & **Recall** & **f1-Score** & **Support** & **Number of Errors** \\ \hline A & 1.00 & 1.00 & 1.00 & 280 & 0 \\ B & 1.00 & 1.00 & 1.00 & 270 & 0 \\ C & 1.00 & 1.00 & 1.00 & 284 & 0 \\ D & 1.00 & 1.00 & 1.00 & 274 & 0 \\ E & 1.00 & 1.00 & 1.00 & 275 & 1 \\ F & 1.00 & 1.00 & 1.00 & 272 & 0 \\ G & 1.00 & 1.00 & 1.00 & 263 & 0 \\ I & 1.00 & 1.00 & 1.00 & 276 & 1 \\ J & 1.00 & 1.00 & 1.00 & 277 & 0 \\ K & 1.00 & 1.00 & 1.00 & 252 & 0 \\ L & 1.00 & 1.00 & 1.00 & 281 & 0 \\ M & 1.00 & 1.00 & 1.00 & 253 & 0 \\ N & 1.00 & 1.00 & 1.00 & 270 & 1 \\ O & 0.99 & 1.00 & 1.00 & 280 & 1 \\ P & 1.00 & 1.00 & 1.00 & 261 & 0 \\ Q & 1.00 & 1.00 & 1.00 & 276 & 1 \\ R & 1.00 & 1.00 & 1.00 & 271 & 0 \\ S & 1.00 & 1.00 & 1.00 & 274 & 0 \\ T & 1.00 & 1.00 & 1.00 & 254 & 0 \\ U & 1.00 & 1.00 & 1.00 & 260 & 0 \\ V & 1.00 & 0.99 & 0.99 & 270 & 0 \\ W & 1.00 & 1.00 & 1.00 & 239 & 1 \\ X & 1.00 & 1.00 & 1.00 & 288 & 0 \\ Y & 1.00 & 1.00 & 1.00 & 260 & 0 \\ Z & 1.00 & 1.00 & 1.00 & 259 & 0 \\ \(\mathcal{E}\) & 1.00 & 1.00 & 1.00 & 271 & 0 \\ \(\mathcal{O}\) & 1.00 & 1.00 & 1.00 & 300 & 0 \\ \hline Accuracy & 1.00 & 1.00 & 1.00 & 7290 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification report of the 32 \(\times\) 32 SVM mode.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Pre-Processing** & **Segmentation** & **Feature Extraction** & **Accuracy** & **Training Time** & **Prediction Time** \\ \hline KNN & \begin{tabular}{c} 64 \(\times\) 64, \\ Normalization \\ \end{tabular} & \begin{tabular}{c} LAB Color Space \& CLAHE, \\ Skin Color detection \\ \end{tabular} & \begin{tabular}{c} HOG, Edge \\ Detection \\ \end{tabular} & 99.9\% & 0 & 188 s \\ \hline KNN & \begin{tabular}{c} 32 \(\times\) 32, \\ Normalization \\ \end{tabular} & \begin{tabular}{c} LAB Color Space \& CLAHE, \\ Skin Color detection \\ \end{tabular} & \begin{tabular}{c} HOG, Edge \\ Detection \\ \end{tabular} & 97.2\% & 0 & 18 s \\ \hline SVM & \begin{tabular}{c} 64 \(\times\) 64, \\ Normalization \\ \end{tabular} & \begin{tabular}{c} Foreground/Background detection \\ using LAB Color space \\ \end{tabular} & HOG & 99.9\% & 43 s & 53 s \\ \hline SVM & \begin{tabular}{c} 32 \(\times\) 32, \\ Normalization \\ \end{tabular} & \
\begin{tabular}{c} Foreground/Background detection \\ using LAB Color space \\ \end{tabular} & HOG & 99.9\% & 6 s & 18 s \\ \hline CNN &
\begin{tabular}{c} 64 \(\times\) 64, \\ Normalization \\ \end{tabular} & None & None & 99.9\% & 23 s & 21 s \\ \hline \hline \end{tabular}
\end{table}
Table 8: Model comparison of all the created models.