## 4 Experiments & Analysis
### Perspective Transform Based on Probabilistic Hough Transform  
The performance of text detection can be impaired by the slanted orientation and abundant surrounding spaces of typical equipment nameplate images. It is crucial to isolate text instances and improve accuracy by removing the cluttered text samples from distorted areas of the nameplate. This study employs a six-stage process to rectify the irregular quadrilateral shape of the nameplate in the image into a regular rectangle. The streamlined procedure is depicted in Figure 4.  
This method requires a nameplate that is easy to extract and has clear characteristics. To minimize the time spent on the experiment, the image is first res  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual Class**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Predicted class} & Positive & True Positive (TP) & False Positive (FP) \\ \cline{2-4}  & Negative & False Negative (FN) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix used to form evaluation metrics.  
Figure 4: (**a**) Picture to be processed. (**b**) Locate the image position to be transformed by detecting the two longest edges of the industrial image. (**c**) All the text information features are enlarged as the final result of projection transformation.  
picture is decreased by a factor of 10 using the Pillow.shape() method. In order to identify the shape of the edge of the grayscale image (see Figure 5a), one must locate the boundaries of the picture and convert the low resolution thumbnail into a grayscale format.  
After the image is binarized, Gaussian smoothing is applied to enhance the capability of the Probabilistic Hough Transform to identify the longer line segments. This is achieved by connecting short line segments to form a longer straight line (see Figure 6). In this research, we employ the Probabilistic Hough Transform method to identify the double lines of the preprocessed image. By utilizing these line segments, it is possible to calculate the corner coordinates of the Cartesian coordinate space for the thumbnail image. Then, the desired corner point of the original image is obtained by multiplying the value of the corner coordinates by ten (see Figure 5b). Once the corner coordinates of the nameplate have been identified, a perspective transformation matrix is applied to the region to convert it into a standardized rectangular shape (see Figure 7a).  
In this study, we have compared the efficacy of two distinct techniques, namely two-line detection and perspective transformation, in the context of nameplate image processing. To validate the performance of these approaches, we have employed the differential binarization model and conducted experiments on nameplate photos. The outcome of the text detection procedure is presented in Figure 7.  
Specifically, our research focuses on the detection and correction of duplicate line segments in conventional images. We utilized a ResNet-18 lightweight backbone in the DB model, which improves accuracy and recognition performance and simplifies the binarization process of the segmented network. The outcome of our investigation, pertaining to the repair and recognition of double-line segments in conventional images, is demonstrated in Figure 7b.  
Figure 5: (a) Display of grayscale diagram. (b) The position of the original picture after enlargement and positioning. The figures describe the process of extracting the four coordinates by using the two longest line segments for edge detection. To reduce irrelevant features, only grayscale images are suitable for this process.