## 4 Experiments & Analysis
### Comparative Experiment  
The effectiveness of conventional text detection techniques is limited to basic text distribution and lacks robustness, as the spacing between characters must not be too close for traditional text recognition. Two popular text detection algorithms, CTPN [32] and EAST [33], possess limitations, as CTPN can only detect horizontally dispersed text while the EAST method produces incorrect predictions for vertical text instances. Moreover, the unique visual characteristics of warped text remain unresolved by both approaches despite their widespread use. To ensure reliable text detection, we leverage DBNet and SAST [34], both of which exhibit high detection accuracy. Deep learning models often incorporate multiple underlying structures, and ResNet was proposed in 2015 to resolve the bottleneck problem associated with the network depth of classic deep learning networks. While it was generally believed that the effectiveness of a network increased with its depth, research has shown that excessively deep networks could worsen the network effect by causing the gradient to vanish. [PERSON]'s ResNet [35; 36] effectively addressed this issue. As a result, we have chosen to employ several iterations of ResNet as the underlying network structure for the detection model. ResNet18 has a fast convergence time and high-accuracy performance compared to other residual network models. A deeper model may yield better results due to its ability to extract more complex features by increasing the network depth. ResNet50, which has a more complex network structure than ResNet18, offers improved accuracy without showing any noticeable degradation. For the differentiable binarization approach, we used both ResNet18 and ResNet50 as the basic networks. In addition, we used the enhanced 50-layer residual network ResNet50-D [36] as the basis for the SAST detector. This was done to speed up the subsequent training phase. We conducted experiments on three different datasets, namely MSRA-TD500, ICDAR2015, and TOTALTEXT, with the aim of training the models immediately and collecting evaluation data. To increase the models' resilience, a novel dataset was generated by utilizing the gathered data and the rectified training images. During the pre-training phase, three separate datasets and two different detectors were used to achieve better weight  
Figure 6: After edge detection, a Gaussian smoothing filter is applied to improve the accuracy of the resulting line segments. The left line segment appears straighter after the smoothing filter is applied, which helps to achieve more accurate results. Figure 7: (**a**) Test results after correction process using DBNet. (**b**) The pictures after correction. ing and detection speed. We performed over 5000 iterations on all pre-trained models, with about 200 images transformed into different perspectives for the new dataset. We evaluated the performance of DBNet using ResNet18 and ResNet50 as backbones on the MSAR-TD500 and MEND datasets. As shown in the Tables 3 and 4, it is not feasible to directly apply the models to complex industrial scenarios. As part of our evaluation of the SAST approach, we conducted a comparative experiment using the ResNet50-D enhanced residual network as the backbone for our system, with the same dataset, to ensure a fair comparison. Specifically, we evaluated our method's efficacy and the difficulty level of the dataset using the ICDAR2015 and TOTALTEXT datasets. The results of the experiment, including precision, recall, and F-measure, are presented in Table 5. Our pre-trained and re-trained models demonstrated excellent performance on these datasets. However, it should be noted that text identification algorithms trained on other datasets may not provide optimal results when used in complex industrial environments. This is because, in addition to the correctness of the character set, line-level text samples must also be collected to address the challenges presented by such settings.