## 5 Conclusions  
We propose a correction module to solve the challenge of unevenly shaped images of industrial equipment nameplates that reduce the accuracy of text identification. The correction module is implemented using the Probabilistic Hough Transform and includes two tasks: perspective image correction and double long line segment recognition. Our method transforms the unusual shapes of nameplates into approximately rectangular shapes, benefiting both character recognition and text detection. It is a fast and simple process that outperforms deep learning approaches in terms of accuracy, speed, and practicality. The proposed method significantly increases the reliability of the detection model by introducing new data labeling training. It is highly suitable for industrial applications and could be integrated into full text recognition in the foreseeable future.  
While the proposed correction module using Probabilistic Hough Transform shows promise in addressing the challenge of unevenly shaped industrial equipment nameplate images, there are still some potential limitations to consider. Firstly, the performance of the module may be affected by the quality of the input images. Images that are too blurry or contain significant noise may not be effectively transformed into rectangular shapes, thus reducing the accuracy of text identification. Secondly, the correction module may not be effective for nameplates with extremely irregular shapes or unusual orientations. In such cases, additional preprocessing steps or more sophisticated techniques may be required to achieve satisfactory results.  
Conceptualization, H.L. and Y.M.; methodology, H.L. and Y.M.; software, H.L.; validation, H.B.; formal analysis, Y.M.; investigation, H.L.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, H.L. and H.B.; writing--review and editing, Y.M. and H.B.; visualization, H.L.; supervision, H.B. and Y.M.; project administration, H.B.; funding acquisition, H.B. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Precision** & **Recall** & **F-Measure** & **FPS** \\ \hline
**Faster-RCNN**  trained in MEND & 0.71 & 0.73 & 0.72 & 14 \\
**Faster-RCNN**  trained on new & & & & \\ pictures in MEND (ours) & 0.85 & 0.75 & 0.80 & 14 \\
**YOLOv3**  trained model in MEND & 0.72 & 0.45 & 0.55 & 25 \\
**YOLOv3**  trained on new & & & & \\ pictures in MEND (ours) & 0.80 & 0.82 & 0.81 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative experiment: Faster RCNN and YOLOv3. As a comparison for text detection in complex environments, experiments were conducted using Faster-RCNN and YOLOv3. First, training was performed on the extraction of features of industrial nameplates separately, and then fine-tuning was carried out on the generated data according to the strategy. The results show that both methods achieved a slight improvement.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Nos. 61932012, 61802019, 61871039, 61906017, and 62006020), the Academic Research Projects of Beijing Union University (Nos. ZK80202003 and ZK10202202), the Beijing Municipal Education Commission Science and Technology Program (Nos. KM202111417007, KM201911417003, KM201911417009, and KM201911417001), the Beijing Union University Research and Innovation Projects for Postgraduates (No. YZ2020K001), and the premium Funding Project for Academic Human Resources Development in Beijing Union University under Grant BPHR2020 DZ02.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.