## 4 Proposed Methodology
### Sign-Character Detection  
Object detection is a computer vision technique for locating objects in an image or video. The initial part of this study is to identify the coordinates of the Bengali character signs of the given image. For detection purposes, the YOLOv4-Tiny model is used. The  
Figure 4: Overview of the proposed end-to-end system. Figure 3: Example of Compound Character. Figure 2: Data distribution of classes. compact version of YOLOv4 is called YOLOv4-Tiny. It is commonly used to detect objects. It is developed to train detection models on low-cost embedded systems such as IoT devices. Furthermore, after training, the YoloV4-Tiny model is quantized to make it more lightweight and faster and to make it implementable on edge devices. YOLOv4-Tiny makes a few modifications to the original YOLOv4 network. To begin with, the number of convolutional layers in the Cross Stage Partial (CSP) backbone is reduced. Furthermore, the number of YOLO layers is decreased from three to two, and there are few anchor boxes for prediction. The model is trained from 29 pre-trained convolutional layers, while YOLOv4 is trained from 137 pre-trained convolutional layers. Manual tuning of the hyperparameters provides the optimal outcome. Table 2 contains details on these parameters. Here, the momentum equals 0.95 for enhancing the gradient's stability. We set decay to 0.0005 to further stabilize the model. The model's steps are set at (24,000, 27,000). As the model detects only character signs, the value 49 is assigned to the class parameter. The filter parameter for the last convolutional layer is set to 162. Cut Mix is assigned 0 in order to conduct a detection operation. The mosaic and random functions are set to 1. The mosaic function creates a mosaic of four images, and the random function dynamically resizes the images between 1/1.4 and 1.4 for each of the ten iterations, which creates a model with a significant level of generalizability. In the final layer, both mosaic and random data augmentation functions are utilized. The batch size is configured to 64 and the subdivision is set to 16 for the model; as a consequence, we may send 64/16 = 4 images into the model in each mini-batch for quicker convergence. The model is trained for 30,000 epochs. Figure 5 illustrates that YoloV4-Tiny has three fundamental components, which are (i) CSPDarknet53-Tiny, (ii) a Feature Pyramid Network (FPN), and (iii) a YOLO Head. For main feature extraction, CSPDarknet53-Tiny is used, which is composed of a convolutional block (Conv) and a CSPBlock. Batch normalization and activation functions are included in convolutional layers. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameter** & **Value** & **Hyperparameter** & **Value** \\ \hline momentum & 0.95 & classes & 49 \\ decay & 0.0005 & Cut\_Mix & 0 \\ steps & 24,000, 27,000 & random & 1 \\ burn\_in & 1000 & mosaic & 1 \\ batch size & 64 & subdivision & 16 \\ channel & 3 & filter & 162 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Final hyperparameters. Figure 5: Overall architecture of sign-detection model (YOLOv4-Tiny). The model is regulated through batch normalization. This replaces the necessity of using dropout layers in the design to avoid overfitting issues. By specifying the variance values, the model improves the normalization of its input. The LeakyReLU activation function in Equation (1) is used in YOLOv4-Tiny, where \(a_{i}\) is a constant parameter. \[y_{i}= \begin{cases}x_{i}&x_{i}\geq 0\\ \frac{x_{i}}{a_{i}}&x_{i}<0\end{cases}  \tag{1}\]  
Cross Stage Partial Block (CSPBlock) divides the Base layer model in half based on the features of Cross Stage Partial Networks (CSPNet). After a series of convolutional operations, the first component is created as a residual edge and the second portion is merged with the previous to generate the final output. The Feature Pyramid Network (FPN) structure may incorporate characteristics from several network levels, including contextual information from deep networks and geometrical information from low-level networks.