# A High Capacity Watermarking Technique for the Printed Document
## 4 Experimental Results and Discussion
### Comparison with the State-of-the-Arts  
This section compares the literature of [21; 22] and the method of this paper. In the watermark capacity, the literature [21] can embed the least watermark information, about 75% of the total number of characters. The watermark information that can be embedded in [22] is about 100% of the total number of characters. The watermark information that can be embedded with the method of this paper is greater than 200% of the total number of characters. When combined with the QR code, more watermark information can be embedded with the proposed method. The watermark capacity comparison of different schemes is shown in Table 3.  
In terms of visual effects, this paper uses three schemes to embed the same watermark information for multiple document images of different fonts, and compares their respective PSNR and SIM scores. In [21], all characters in the scheme have to be flipped, so the text image after embedding the watermark has the largest change compared with the original text image. In the literature [22], there is no adjustment part, so the number of characters that need to flip the pixels is less than the literature [21] scheme. Therefore, the overall visual effect of the literature [22] is better. Compared with the literature [22], our algorithm requires fewer characters to flip the pixels, and because of the introduction of the quantization function, the pixel flipping range has a gradient effect, so that the character only needs to flip fewer pixels to embed the watermark. Tables 4 and 5 compare the PSNR and SIM scores of existing schemes in different fonts.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Num** & **Author-Year** & **Arial 14 pt** & **TNR 14 pt** & **LiSu 14 pt** & **KaiTi 14 pt** & **Century 14 pt** \\ \hline
1 & [21]-2008 & 25.2 & 24.4 & 24.0 & 25.6 & 26.9 \\
2 & [22]-2019 & 27.9 & 26.3 & 25.8 & 28.4 & 26.7 \\
3 & Proposed method & 31.3 & 30.9 & 28.2 & 33.1 & 31.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The PSNR value of different models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Num** & **Author-year** & **Feature** & **Capacity** \\ \hline
1 & [21]-2008 & pixel & \textless{}75\% \\
2 & [22]-2019 & pixel & \textless{}100\% \\
3 & Proposed method & pixel & \textgreater{}200\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of watermark capacity.  
Figure 7: Embedded and extracted QR code.  
In terms of robustness, the literature [21; 22] are almost the same. The algorithm proposed in this paper is affected by the quantization function. When the \(\delta\) is larger, the robustness is better, and the bit error rate of single character is lower during watermark extraction. However, since the method of this paper has a larger watermark capacity, it is possible to reduce the impact of the bit error rate by increasing the error correction and verification schemes, thereby improving the robustness of the watermark. Furthermore, this paper also proposes to use the QR code as the watermark information, and use the error correction and verification functions of the QR code to improve the robustness of the watermark. Figure 7 shows the case of using a QR code as watermark information. In addition, our algorithm also has a good effect in anti-scaling and anti-noise. Table 6 shows the bit error rate under various attack conditions. This paper selects a text image to embed 100 bits watermark information with three methods. ERR-n represents the number of the error bits.