# Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
## 4 Experiments
### Experimental Results  
To validate the effectiveness of our proposed approach, we conducted comprehensive experiments comparing our model against leading state-of-the-art (SOTA) methods in video prediction. The evaluation metrics were carefully chosen to assess both the visual quality of predicted sequences and the computational efficiency of the models. As shown in Table 1, the results reveal substantial improvements across all evaluation metrics. The Structural Similarity Index (SSIM) of 0.913 demonstrates a 1.2% gain over the best baseline (DMVFN), indicating a superior ability to preserve structural details in the predicted frames. The Peak Signal-to-Noise Ratio (PSNR) of 30.2 dB reflects enhanced reconstruction quality, with a 1.1 dB improvement over DMVFN. Notably, our model achieves these gains while utilizing fewer computational resources, with a 9.5% reduction in training time and a 9.2% decrease in memory usage compared to the most efficient baseline. The improved performance can be attributed to several key factors. First, our modified ConvGRU architecture is more effective at capturing the temporal dynamics of handwritten digits, leading to more accurate predictions of digit trajectories and transformations. Second, the feature decoupling mechanism enhances the processing of spatial information, ensuring better preservation of digit characteristics across predicted frames. Lastly, the optimized gating structure minimizes information loss during prediction, resulting in superior temporal consistency. Qualitative analysis of the predicted sequences shows that our model maintains significantly clearer digit representation and style consistency compared to baseline methods, especially in extended sequences. This is particularly evident in scenarios involving multiple digits with complex interactions, where our model successfully preserves individual digit characteristics while accurately predicting their relative movements and potential overlaps. The results in Table 2 demonstrate the practical applicability of our approach across various real-world scenarios. In standard conditions, the system achieves 95.3% accuracy with a processing time of 15.2 milliseconds per frame, making it suitable for real-time applications. The model maintains robust performance across different environmental conditions, with accuracy rates of 91.8% for connected digits and 89.7% under challenging lighting conditions. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Scenario** & **Accuracy (\%)** & **SSIM** & **PSNR (dB)** & \begin{tabular}{c} **Processing** \\ **Time (ms)** \\ \end{tabular} & \
\begin{tabular}{c} **Memory** \\ **Usage (MB)** \\ \end{tabular} &
\begin{tabular}{c} **Frame Rate** \\ **(fps)** \\ \end{tabular} \\ \hline \multicolumn{5}{l}{**Standard Writing Conditions**} \\ \hline Isolated digits & 95.3 \(\pm\) 0.4 & 0.913 & 31.2 & 15.2 & 256 & 65.8 \\ Two-digit sequence & 94.1 \(\pm\) 0.5 & 0.902 & 30.5 & 16.8 & 268 & 59.5 \\ Three-digit sequence & 92.8 \(\pm\) 0.6 & 0.894 & 29.8 & 18.4 & 275 & 54.3 \\ Four-digit sequence & 91.5 \(\pm\) 0.7 & 0.885 & 29.1 & 20.1 & 282 & 49.8 \\ \hline \multicolumn{5}{l}{**Writing Style Variations**} \\ \hline Cursive writing & 93.1 \(\pm\) 0.5 & 0.895 & 30.1 & 16.8 & 264 & 59.5 \\ Connected digits & 91.8 \(\pm\) 0.7 & 0.882 & 29.4 & 18.5 & 271 & 54.1 \\ Fast writing & 90.5 \(\pm\) 0.8 & 0.875 & 28.9 & 17.9 & 268 & 55.9 \\ Slow writing & 94.2 \(\pm\) 0.4 & 0.908 & 30.8 & 16.5 & 262 & 60.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comprehensive performance analysis across different scenarios and writing styles.