## 4 Experiments  
### Datasets and Evaluation Criteria  
In this study, we opted to utilize scene text datasets instead of logo text datasets for several key reasons. Firstly, logo text datasets are relatively scarce, and the volume of annotated data is limited, which poses certain limitations in large-scale training and the assessment of generalization capabilities. Conversely, scene text datasets offer a greater variety of samples, encompassing complex backgrounds and diverse text layouts and thus better simulating the intricate situations in which logo texts might appear in reality. Secondly, scene text shares a substantial similarity with logo text in numerous aspects, particularly in the high degree of integration between text and backgrounds. Logo texts often appear against complex backgrounds as well, and the challenge in the removal task is akin to that encountered with scene text removal, suggesting that employing scene text datasets can effectively evaluate the performance of the model in logo text removal tasks. Finally, the scene text datasets contain a wide range of different types of text instances, such as curved text and overlapping text, which can serve as rich training data for the model, enhancing its adaptability and robustness when tackling the logo text removal task. To validate the effectiveness of MTF-STTR, this study employed two publicly available datasets. For the text detection network comparisons, the CTW1500 dataset [4] was used, while for the overall network framework comparisons, the SCUT-Syn dataset [27] was utilized. The CTW1500 dataset primarily focuses on curved text and comprises 1000 training images and 500 test images. The text instances within the dataset are annotated at the level of complete lines of text, meaning that the annotation does not limit itself to single characters or words but extends to entire lines of text. An example of the CTW1500 dataset is shown in Figure 6. The emphasis on line-level annotations in the CTW1500 dataset makes it suitable for evaluating algorithms capable of handling complex text layouts, including those involving curvature and overlapping text. The inclusion of both training and testing subsets allows researchers to assess the generalizability and performance of their models on real-world data. The figure referenced would likely illustrate a sample image from the dataset, showcasing the challenges faced by text detection algorithms in recognizing and segmenting curved text strings. The SCUT-Syn dataset is a synthetic dataset that includes a training set of 8000 images and a test set of 800 images. It incorporates real-world data collected from ICDAR-2013 [28] and ICDAR MLT-2017 [29]. An example of the SCUT-Syn dataset is shown in Figure 7.  
The mean squared error (\(MSE\)) is a widely used image quality metric that quantifies the difference between the reconstructed or processed image and the reference image. \(MSE\) evaluates the degree of error between two images by calculating the square of the differences between corresponding pixels and averaging them. A lower numerical value indicates that the reconstructed image is closer to the reference image. The formula is as shown in Equation (12), where \(N\) represents the total number of pixels in the image, and \(y(i)\) and \(x(i)\) denote the pixel values of the original and processed images, respectively. \[MSE=\frac{1}{N}\sum_{i=1}^{N}\left(y(i)-x(i)\right)^{2} \tag{12}\]  
The peak signal-to-noise ratio (\(PSNR\)) is an extensively used objective criterion for assessing image quality, mainly employed to quantify the difference between a reconstructed or compressed image and the original image. \(PSNR\) measures reconstruction accuracy by calculating the \(MSE\) between original and processed images, with higher values indicating that the reconstructed image quality is closer to the original and the distortion is smaller. Specifically, PSNR characterizes the ratio of the peak signal strength to the noise level in the image.