## 3 Methodology
### Experiments  
For our experiments, we have listed the complete details about the configuration as well as the training procedure as follows.  
Configuration and Training  
A desktop computer manufactured by System Manufacturer, with a product name \"Model System\", was used in the experiments. The machine has 32 GB of RAM, a VGA-compatible GPU, and an Intel(R) Core(TM) i9-1092X CPU with the clock speed running from 1.2 GHz to 4.8 GHz. To facilitate efficient computation, CUDA 12.2 was used for GPU acceleration. For feature extraction and hand landmark keypoints, the MHL model was used. This setup ensured optimal performance during both training and inference.  
We used the TensorFlow framework to train the models. The training process involved splitting the datasets into training and validation sets, with a typical split of 80 for training and 20 for validation and a fivefold cross-validation. A batch size of 32 was used, and the models were trained for 100 epochs, with early stopping applied to prevent overfitting. The learning rate was initialized to 0.001. For training and validation losses, we used the categorical cross-entropy function, which ensures maximum learning. To iteratively adjust and update the weights, we used the Adam optimizer. This ensured efficient gradient descent, and the learning rate was tuned for optimal performance across all the model training.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Year** & **Domain** & **Depth** & **Parameters** & **Input** & **Special Feature** \\ \hline InceptionV4 [18] & 2017 & Image Classification & 42 & 43.8 M & 299 \(\times\) 299 & Factorized convolution-based \\ NanoViT [19] & 2022 & Vision Transformers & 12 & 5.4 M & 224 \(\times\) 224 & Lightweight vision transformer \\ EfficientNet [20] & 2019 & Image Classification & 30 & 5.3 M & 224 \(\times\) 224 & Compound scaling-based \\ EdgeNeXt [21] & 2022 & Lightweight Networks & 12 & 5.6 M & 224 \(\times\) 224 & Hybrid CNN-transformer \\ ConvNeXt [22] & 2022 & Image Classification & 50 & 88.0 M & 224 \(\times\) 224 & Fine-tuned ResNet50 architecture \\ MobileNetV3 [23] & 2019 & Image Classification & 16 & 5.4 M & 224 \(\times\) 224 & NAS-optimized \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary table presenting key information about the models used in the experiments.  
For all the datasets, the data split involved allocating 80% of the total gesture clips per class for training, while the remaining 20% was reserved for testing, allowing the model to learn from a considerable portion while also evaluating performance on unseen data, which is critical for gauging generalization and preventing overfitting. This split affects the balance between training efficacy, ensuring robustness and reliability in deep learning models. After the training process, we evaluated the models' performance by assessing their accuracy on unseen data; see Section 4.1. Furthermore, we conducted live testing scenarios both indoors and outdoors and cross-hand gestures (as datasets are mostly biased) [46; 47] to provide a more comprehensive evaluation of the models' effectiveness in real-world environments; see Section 4.2.