# From Classic to Cutting-Edge: A Near-Perfect Global Thresholding Approach with Machine Learning
## 2 Materials and Methods
### Proposed Solution
Table 6 compares the results of the proposed method with those of state-of-the-art global thresholding methods (introduced in Section 1.2) on the H-DISCO 2014 dataset. Table 7 shows that the proposed method outperforms state-of-the-art neural-network-based methods (introduced in Section 1.2) on the DIBCO 2019 dataset, but it also shows that, when averaging on all DIBCO datasets, the proposed method cannot compete. This shows that global methods are not obsolete and that local methods are not always the better choice, even when time and resources are not taken into consideration. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Feature Set** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **Full** \\ \hline Feature generation runtime (ms) & 2 & 0 * & 0 * & 2 & 2 & 0 * & 2 \\ \hline ML regression runtime (ms) & 230 & 340 & 415 & 225 & 214 & 246 & 251 & 234 \\ \hline Total image binarization runtime (ms) & 232 & 340 & 415 & 227 & 216 & 248 & 253 & 236 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average processing time for an image in the dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Proposed** & & & & & & & & & \\
**Solution** & & & & & & & & & \\
**with Feature** & & & & & & & & & \\
**Set** & & & & & & & & & \\ \hline \(\overline{FM}\) (\%) & 81.56 & 81.65 & 62.30 & 81.75 & 81.73 & 81.73 & 81.75 & 81.55 \\ \hline \(\overline{FM_{r}}\) (\%) & 99.70 & 99.79 & 70.58 & 99.99 & 99.97 & 99.93 & 99.99 & 99.74 \\ \hline \(\overline{PSNR}\) & 27.01 & 27.37 & 18.17 & 27.50 & 27.48 & 27.49 & 27.50 & 27.18 \\ \hline \(\overline{PSNR}\) (\%) & 92.94 & 93.96 & 69.22 & 93.99 & 93.95 & 93.96 & 93.99 & 93.19 \\ \hline \(\overline{MSE}\) & 3202 & 3236 & 8156 & 3244 & 3250 & 3236 & 3244 & 3271 \\ \hline \(\overline{MSE}_{r}\) (\%) & 87.50 & 89.68 & 70.58 & 90.60 & 90.49 & 90.50 & 90.60 & 88.40 \\ \hline
**[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline \(\overline{FM}\) (\%) & 81.75 & 67.11 & 64.57 & 63.65 & 61.68 & 64.88 & 53.05 & 53.67 \\ \hline \(\overline{FM_{r}}\) (\%) & 100 & 79.24 & 73.10 & 70.62 & 73.40 & 77.13 & 64.48 & 58.90 \\ \hline \(\overline{PSNR}\) & 27.50 & 15.89 & 18.45 & 18.63 & 15.35 & 15.12 & 11.54 & 14.41 \\ \hline \(\overline{PSNR}_{r}\) (\%) & 93.99 & 60.21 & 65.84 & 67.67 & 55.40 & 57.91 & 46.76 & 59.54 \\ \hline \(\overline{MSE}\) & 3244 & 10,289 & 9528 & 7900 & 11,790 & 10,819 & 16,322 & 12,205 \\ \hline \(\overline{MSE}_{r}\) (\%) & 90.6 & 35.86 & 45.88 & 41.01 & 30.67 & 33.73 & 24.22 & 37.68 \\ \hline
**[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline \(\overline{FM}\) (\%) & 66.51 & 68.59 & 62.34 & 60.13 & 59.90 & 50.22 & 58.47 & 60.23 \\ \hline \(\overline{FM_{r}}\) (\%) & 78.86 & 79.24 & 73.10 & 70.62 & 73.40 & 77.13 & 64.48 & 58.90 \\ \hline \(\overline{PSNR}\) & 16.75 & 16.94 & 14.77 & 13.40 & 13.20 & 11.29 & 12.85 & 12.04 \\



## 4 Discussion

One thing to note is that the proposed solution's global approach offers various benefits in terms of robustness and computational efficiency. By operating in a global context, rather than depending on local relationships, the algorithm is less affected by noise or small-scale variations that could affect the results, making it especially useful for tasks where global patterns matter more than detailed local structures. This also results in simpler computations, meaning faster processing and lower complexity, which is ideal for real-time or resource-constrained applications, like embedded systems. Additionally, since the solution does not depend on spatial relationships between samples, it generalizes well to various signals that require separation in low- and high-value groups, beyond just bi-dimensional images.