# Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
## 4 Experimental Results and Analysis
### Dataset and Evaluation Metrics  
(1) Synthetic dataset  
Since the degree of the show-through phenomenon varies across different real-life scenes, we simulated two different degrees of bleeding phenomena based on different Gaussian blur levels to demonstrate the effectiveness of our proposed method for different levels of show-through phenomena. We simulated the real bleeding phenomenon by adjusting the Gaussian template. Ultimately, a kernel size (ksize) of 5 \(\times\) 5 with a standard deviation (sigma) of either 1 or 0.5 was chosen, as depicted in Figure 10.  
S-color adopts a hybrid linear show-through model, where the show-through component of S-color is achieved by flipping the image horizontally during the image-preprocessing stage. The specific operation in Equation (2) is as follows: the flipped image is subjected to Gaussian blur using the function \(\phi(\cdot)\), with a kernel size of 5 \(\times\) 5 and a selectable standard deviation of 1 or 0.5. Additionally, a randomly selected parameter \(\alpha\) with a value between 0.1 and 0.2 is used to simulate a real bleeding phenomenon. The S-color1.0 and S-color0.5 datasets each consist of 1000 pairs of training images and 100 pairs of testing images. Each dataset consists of show-through images and non-show-through images, and there is no exact correspondence between these two pairs of images. This means that the model needs to learn how to transform images from one domain to another, rather than simply performing a straightforward mapping. Real dataset  
We evaluated the proposed method using the Media Team Document Database (MTDB) [27], which contains various types of document images such as address lists, advertisements, articles, business cards, music sheets, and maps. In our experiments, we focused on 10 out of the 19 predefined categories in MTDB that, more than the others, contain text documents exhibiting show-through phenomena. The scans from the MTDB were performed using a Hewlett Packard ScanJet scanner and DeskScan ll 2.3.1a software, with color-adjustment parameters set to distance 0, direction 0, saturation 50, normal emphasis curve, resolution of 300 dpi, true-color mode, no image sharpening, and photo style with highlight and shadow values set to 125. We first randomly cropped images of size 256 \(\times\) 256 from higher-resolution images (\(2000^{+}\times 3000^{+}\)) and then selected 200 images with obvious show-through phenomena as our test set. The access link for MTDB is as follows: [[https://github.com/liangzongbao/CDSR-CycleGAN/tree/main/data/MDTB](https://github.com/liangzongbao/CDSR-CycleGAN/tree/main/data/MDTB)]([https://github.com/liangzongbao/CDSR-CycleGAN/tree/main/data/MDTB](https://github.com/liangzongbao/CDSR-CycleGAN/tree/main/data/MDTB)) (25 May 2024). The ICDAR-SROIE dataset [28] was created to support the ICDAR 2019 competition and aims to evaluate the performance of scene text-recognition and information-extraction algorithms on real business documents, specifically receipts and invoices. This dataset consists of 1000 complete scanned receipt images, with each image containing approximately four key text fields such as item names, unit prices, total costs, etc. The text primarily consists of numbers and English characters, and some images may have issues like stains, noise, and blurriness. In an approach similar to that used for MTDB, we first randomly cropped images of size 256 \(\times\) 256 from a subset of higher-resolution images, and then selected 30 images with noticeable stains as our test set to validate the generalizability of our proposed method in this paper. The MS Tain Bo Cuailnge (MS) [9] is an ancient manuscript. It was created by [PERSON] (1817-1880), one of the last professional Gaelic scribes. He produced numerous Gaelic manuscripts, especially during the 19 th century. We selected the first and thirteenth  
Figure 10: Example of synthesized show-through image. pages from the book, then cropped them to a size of 256 \(\times\) 256 for testing.