# Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
## 4 Experimental Results and Analysis
### OCR Recognition Analysis  
This section aims to validate the effectiveness of the proposed algorithm in removing document show-through and to explore its potential applications in practical engineering. A series of experiments were conducted, and the experimental result images based on the S-color0.5 dataset were tested using the open-source Paddle Paddle v4 version for OCR testing, with the test address available at [[https://github.com/PaddlePaddle/PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)]([https://github.com/PaddlePaddle/PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) (8 May 2024). In this section, the show-through-removal results of the proposed algorithm are presented and compared with the results of several comparison algorithms for OCR recognition. The OCR-recognition results are shown in Figure 17, indicating that the proposed algorithm demonstrates significant effectiveness in removing document show-through. By applying this algorithm to process the experimental result images, the document show-through effect can be successfully eliminated, greatly improving the readability and accuracy of the images. The first row of the image in Figure 17 is used as an example for evaluation of the proposed method and comparison with other methods. The OCR-recognition comparison results, as shown in Figure 18, consist of three rows: the first row presents the experimental result image, the second row displays the recognition result visualization, and the third row shows the output text result. The results of the DAD and DeepOtsu methods are missing some text content, while YTMT incorrectly recognized the show-through content as the correct text. A comparison reveals the significant effectiveness of the proposed algorithm in eliminating document show-through. By applying this algorithm to process the experimental result images, the document show-through effect can be successfully eliminated, greatly improving the readability and accuracy of the images. Taking the  
Figure 17: Example of OCR recognition comparison. sample in Figure 16 as an example, incomplete data from the top row and right side were not included in the evaluation. Table 5 presents a quantitative analysis of OCR recognition based on counts of the number of words and characters and the number of correct words and characters. The number of words in the ground truth is 65, and the number of characters is 303, so the closer the measured quantities are to these values, the better the recognition performance. In terms of word count, some adjacent words are recognized as a single word due to being too close together; for example, \"a bag to\" is recognized as \"abagto\", which reduces the word count. In terms of characters, it was observed that \"n\", \"r\", and \"c\" are prone to recognition errors. Specifically, the proposed method demonstrates better recognition accuracy compared to several other methods. This finding demonstrates the practical significance of the proposed algorithm in solving the document show-through problem. Furthermore, the potential advantages and possibilities  
Figure 18: Visualization of OCR recognition for different methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & & \\ \cline{3-8}  & **Index** & **Ground Truth** & **Show-Through Image** & **DeepOtsu** & **DAD** & **YMTT** & **Ours** \\ \hline \multirow{2}{*}{Numbers} & Words & 65 & 76 & 46 & 59 & 71 & 65 \\  & Characters & 303 & 353 & 266 & 295 & 375 & 303 \\ \hline Correct & Words & 65 & 48 & 36 & 54 & 32 & 64 \\ Numbers & Characters & 303 & 290 & 244 & 293 & 295 & 303 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative analysis of OCR recognition. of applying this algorithm in practical engineering will be explored. For example, in fields such as digital archive management, print quality control, and image post-processing, this algorithm can provide a reliable solution for relevant applications.