## 2 Related Works
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Article** & **Contribution** & **Shortcomings and Future Improvements** \\ \hline [PERSON] et al. [12] & They put forward a model for multi-directional nameplate text recognition, which converts curved text into nearly horizontal text. & Unable to process extremely long text, lack of long text data set verification. \\ \hline [PERSON] et al. [23] & This study proposes a method for using the improved Faster-RCNN as a label positioning algorithm. & Typical nameplates have visible locations, so basic target detection algorithms are adequate and faster. \\ \hline [PERSON] et al. [24] & The study proposed a similar edge detection method to locate and transform the position of nameplates. & CTPN is a powerful text detection algorithm, but now there are stronger text detection algorithms available. \\ \hline [PERSON] et al. [14] & This study suggests a framework for signboard text detection and recognition in natural environments. & The accuracy of model identification needs to be improved now, and the speed of experimental results is not improved. \\ \hline [PERSON] et al. [25] & The study proposes a region-based method for providing good coverage of the corners of plaques. & Despite the algorithm’s impressive performance, its limited background fails to prove its applicability in complex industrial settings. \\ \hline [PERSON] et al. [16] & Hough transform is incorporated into the deep learning framework, and a new semantic line detection method in natural scenes is proposed. & Hough transform is common in line segment and simple figure prediction, and this improved method can be used to predict other shapes in complex backgrounds. \\ \hline [PERSON] et al. [17] & A robust method for detecting aging sockets to ensure normal use of IC chips in sockets was proposed in the article. & More images need examining, applying this method to different types of diffuse reflection; analyzing the properties of diffused reflected light in detail is also necessary. \\ \hline [PERSON] et al. [18] & A method for counting incomplete or fragmented red blood cells was proposed in the article, which was preceded by image preprocessing. & Despite saving researchers a lot of time, this method still has significant errors in the context of medicine and requires more precise performance. \\ \hline [PERSON] et al. [19] & A visual-based lane tracking method was proposed in this study. & Errors in traffic scenarios can be deadly, making this method challenging to use in practical applications. \\ \hline [PERSON] et al. [20] & An efficient method for detecting highway lanes was proposed in this study. & Method detects both curved and straight lane lines but needs enhanced accuracy for real road conditions. \\ \hline [PERSON] et al. [21] & The study used the Probabilistic Hough Transform for clustering, effectively correcting skewed documents. & Detecting densely packed text can be challenging, requiring additional processing. Although it may be effective in clean environments, it has not been demonstrated to be robust across varied backgrounds. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Contributions and improvements from related works. The field of deep learning is constantly striving to optimize both speed and accuracy. The integration of the Hough Transform algorithm into various applications is evidence of this, as seen in studies such as the one on the deep fuzzy hash network for image retrieval efficiency [26; 27]. The method suggested in this study uses the Progressive Probabilistic Hough Transform for corner detection in industrial nameplates. The results of this approach show higher recognition accuracy and faster recognition time compared to traditional methods. Image pre-processing using this technique can improve the overall performance of end-to-end text recognition algorithms. The Hough transform has proven to be a valuable tool for detecting and identifying shapes in images, making it a widely used technique in machine learning applications.