## 1 Introduction
These techniques are employed to discern and separate entities or forms that are not readily perceptible in the original picture. The threshold segmentation technique uses a predetermined level to determine the edges of an item or shape in the image. Alternatively, frequency domain analysis techniques leverage frequency-based filters to recognize a certain trait in an image. By using threshold segmentation or frequency domain analysis techniques to standardize the image, important details can be made more visible and easier to detect. While frequency domain analysis and threshold segmentation are useful techniques in image analysis, they also have certain limitations when applied to industrial nameplate recognition. One of the main drawbacks of threshold segmentation is that it requires a predetermined threshold level, which can vary depending on the specific image being analyzed. This can lead to inaccuracies in detecting and separating key elements in the image,  
Figure 1: Challenges of OCR in industrial environments. Optical Character Recognition (OCR) faces several challenges in industrial settings. It includes the ability to recognize text in a variety of sizes, fonts, and orientations, as well as the capability to identify text in images that have low resolution and are affected by noise, such as those with multiple light sources or bright lighting. Nameplate image: mixed Chinese-English scene. Provides equipment specifications and performance information. These difficulties must be overcome to enable accurate and reliable text recognition in industrial environments. particularly in cases where there is a lot of noise or distortion. Similarly, frequency domain analysis can be limited by its dependence on frequency-based filters. In certain scenarios, these filters may not effectively capture the necessary features of the image, leading to inaccurate or incomplete recognition of the nameplate. As a result, these techniques may need to be supplemented with other methods in order to achieve accurate and reliable nameplate recognition in industrial scenarios. Implementing the aforementioned techniques either alone or in tandem can lead to improved image data extraction. However, in industrial settings, due to their often intricate and unpredictable nature, nameplate recognition technology is somewhat limited in its capabilities [10]. Our research introduces a novel probability-based Hough transform method for corner detection in industrial nameplates. The main research objective is to improve the accuracy and speed of corner detection in complex scenarios, by reducing the negative sample impact. Furthermore, our method offers proper image pre-processing, which can contribute to the improvement of end-to-end text detection algorithms. To enhance the stability of detection models, we also provide challenging industrial nameplate datasets for training. In the second section, we provide a literature review of the Hough transform and related research in the field of computer vision. This includes a discussion of its applications, strengths, and limitations. The third section describes our proposed method, which involves corner recognition, projection transformation, and associated information derived from the progressive probability Hough transform. The fourth section presents the experimental evaluation of our method, which includes a data collection process and a comparison of text detection results obtained from the experimental data. The evaluation demonstrates the effectiveness and superiority of our proposed method in terms of recognition accuracy and efficiency. Finally, we summarize our findings and contributions in the conclusion section, highlighting the potential implications of our research in the field of computer vision.