## 5 Experiments and Results
### Quantitative Analysis  
Precision and recall metrics are used to measure the effectiveness of the first-stage model bounding box predictions, which are given by Equations (13) and (14). \[Precision=\frac{TP}{TP+FP} \tag{13}\]  
\[Recall=\frac{TP}{TP+FN} \tag{14}\]  
To evaluate the weight-reduced and attention networks, Table 2 shows the experimental result obtained by constructing a double-stack hourglass network, where the number of parameters in the table represents the total number of parameters. The mean average precision is calculated as the mean over all the 183 classes of objects. The number of parameters is greatly reduced using the depthwise separable convolution. Also, the inference time is improved by converting the model into TensorFlow Lite for mobile and embedded devices. The model weights are converted into 16-bit floating point and 8-bit unsigned integer values using the quantization technique. This will reduce the model size by 2 and 4, with the least impact on model accuracy and latency. The modified model has a mAP of 81.9%, which is a 4.4% improvement over the baseline centernet model and a 6.37% improvement over the YOLOv3 [59] model. The YOLOv9e [60] provides a mAP of 83.3%, surpassing all the models. Default training parameters are used as per the original papers \begin{table}
\begin{tabular}{c|c c c c c} \hline \hline
**Easy Samples** & **CTC Output** & **Decoded Label** & **Hard Samples** & **CTC Output** & **Decoded Label** \\ \hline
**EXT** & [30; 49; 34; 45] & EXIT & **NEEP** & [35; 28; 45; 20] & XeV \\ \hline
**FTK\({}_{\text{ill}}\)** & [53; 53; 36; 12] & 11 Km & **YOLO** & [28; 42] & CQ \\
**Okeo** & [40; 10; 4; 19; 14] & Oketo & **YOLOv9e** & [] & \\
**SP2\({}_{\text{Kim}}\)** & [55; 54; 10; 12] & 32 km & **YOLO** & [50; 1176] & YOLO \\ \hline
**FTZS** & [126; 407] & **TIL** & **YOLO** & [55; 19; 40; 13; 20] & 3 tOnu \\ \hline
**SP2\({}_{\text{Kim}}\)** & [362; 564; 407; 1304] & **TIL** & **YOLO** & [28; 60; 48; 10] & C8 Wk \\ \hline \hline \end{tabular}
\end{table} 
Table 1: OCR model predictions for easy and hard samples. Figure 25: Comparison results of YOLO v3 vs. our model for worst-case conditions. (**a–d**) YOLOv3 predictions; (**e–h**) Our model predictions. for Yolo training without changing any hyperparameters. The YOLO models were used to compare our model with the state-of-the-art models, and the tuning of hyperparameters is out of the scope of this paper. The inference time is calculated for the heatmap prediction stage and the post-processing stage after GPU warmup. The post-processing stage time depends on the number of text characters present in the input image. The mean time of a single batch is noted in the below table. Table 3 shows the average precision values for the top five object classes, which are the text classes since the number of objects in these classes is comparatively higher. Table 4 shows the average inference times for a batch of validation datasets. The inference times of the models are 0.174 s for the baseline model, 0.23 s for the YOLOv3 model, 0.157 s for our model, and 0.039 s for the YOLOv9 model. There is room for improvements in the model speed for our anchorless model in the future. Nevertheless, the YOLOv9 model has better accuracy and faster inference time compared to any of the models under study but comes with the cost of a higher number of parameters. Figure 26 illustrates the total loss curves for the object detection model. The validation loss improved until epoch 350, then the training was stopped at 500 epochs to avoid overfitting, and the best weights for the validation loss were saved. Table 5 shows the average inference times for a batch of validation datasets. The inference time of the model is 0.031 s for the OCR model.