# Compact Spatial Pyramid Pooling Deep Convolutional Neural Network Based Hand Gestures Decoder
## 7 Results
The proposed gesture decoder algorithm needs faster prediction output to apply in real-time in the gesture decoder systems. Table 8 shows a comparative run-time of the proposed algorithms in real-time based on the generated data from Algorithm 1. The setup was followed, as described at the beginning of this Section 7. Now, the proposed 3 models perform relatively well. However, it is noticeable the run-time for a video sequence containing 4 gestures have \(19.30\pm 1.32\) s average. On the other hand, the pruned version was sp \begin{table}
\begin{tabular}{c c c} \hline \hline
**Original Parameters** & **After Pruned** & **Pruned Parameters** \\ \hline
22,498,973 & 7,323,869 & 15,175,104 \\ Total Compression (\%) & 67.45\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Deep Convolutional Network Pruning Statistics. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Original Model**} & \multicolumn{3}{c}{**Pruned Model**} & \multicolumn{3}{c}{**Convolutional Spatial Pyramid Pooling Model**} \\ \hline
**Class** & **Precision** & **Recall** & **\(f\)1-Score** & **Support** & **Precision** & **Recall** & **\(f\)1-Score** & **Support** & **Precision** & **Recall** & **\(f\)1-Score** & **Support** \\ \hline
0 & 0.95 & 0.97 & 0.96 & 146 & 0.96 & 0.95 & 0.96 & 137 & 0.9 & 1 & 0.95 & 140 \\
1 & 0.93 & 1 & 0.96 & 137 & 0.97 & 0.94 & 0.96 & 125 & 0.  
having \(9.34\pm 1.37\) s average. This is predictable, as the pruned model has less weight parameters to calculate in real-time. Moreover, in real-life, some of the server-based application has the option for multi-GPU based parallelization. The proposed SPP-based DCNN was also implemented as a multi-GPU based in this experiment and had an astonishing \(0.013\pm 0.019\) s average run when compared to the other models. Figure 11: Filters of the first six layers of the proposed convolutional neural network, ranked by weight II normalization. Figure 12: Examples of mis-labeling by all three proposed models; (**A**) original model; (**B**) pruned model; and, (**C**) spatial pyramid pooling model  
Table 9 shows the results of multi-GPU based parallelization in greater detail. Here, the 6 gesture sequence video or 6 consecutive alphabets finger-spelling takes an average of \(267\pm 2.13\) ms to decode per frames. Based on these results, the proposed algorithm is fast enough to decode gestures in the real world. Table 10 shows the results of gesture decoding by the proposed algorithm while using a video with various frame rates. The mean majority accuracy was counted based on Equation (12) from Section 6. This provides an idea of the overall ability of the proposed decoding algorithm. Even at 60 fps, the decoder gives 99% accuracy. The decrease in accuracy with increased fps can be explained by the transitional frames between gestures. However, as this error is minimal, the algorithm can cope with this minuscule source of error. Based on this experiment, 30 fps is the recommended speed for fast real-world decoding for practical applications. Our proposed decoder or ASL finger-spelling system usually 100% accurate in various finger-spelling or gesture decoding. However, the mean majority in rows shows that the gradual rise of error can be cumulative and might make wrong classification in future. However, more training with diverse dataset and applying proposed algorithm in recommended 30 fps will held up the reported accuracy of any ASL-finger-spelling gesture decoding in real time.