# Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
## 4 Experiments
### Ablation Experiment  
Table 3 presents the results of the ablation study to evaluate the contributions of individual components in the proposed model. The removal of the temporal branch resulted in a significant performance drop, with accuracy decreasing from 95.3% to 91.2%, highlighting the critical role of temporal modeling in capturing dependencies for accurate trajectory predictions. Similarly, excluding the spatial branch reduced accuracy to 90.8%, demonstrating its importance in retaining spatial details and ensuring the clarity of predicted frames. The attention mechanism also proved crucial, as its removal caused a performance decline to 92.4%, indicating its effectiveness in emphasizing salient features and handling complex digit sequences. Feature fusion further showed its significance, with accuracy dropping to 89.7% in its absence, underlining the necessity of integrating temporal and spatial information for coherent predictions.  
\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{6}{l}{**Environmental Challenges**} \\ \hline Low light & 89.7 \(\pm\) 0.9 & 0.865 & 28.3 & 19.8 & 273 & 50.5 \\ Motion blur & 88.4 \(\pm\) 1.0 & 0.858 & 27.9 & 20.5 & 275 & 48.8 \\ Background noise & 90.2 \(\pm\) 0.8 & 0.871 & 28.6 & 19.1 & 270 & 52.4 \\ Perspective distortion & 87.9 \(\pm\) 1.1 & 0.852 & 27.5 & 21.2 & 278 & 47.2 \\ \hline \multicolumn{6}{l}{**Special Cases**} \\ \hline Overlapping & 89.5 \(\pm\) 1.0 & 0.867 & 28.4 & 20.3 & 276 & 49.3 \\ Digits & 92.3 \(\pm\) 0.6 & 0.889 & 29.7 & 17.5 & 267 & 57.1 \\ Mixed styles & 91.1 \(\pm\) 0.8 & 0.878 & 29.0 & 18.8 & 272 & 53.2 \\ Non-standard & 88.7 \(\pm\) 1.2 & 0.861 & 28.1 & 20.8 & 277 & 48.1 \\ \hline \multicolumn{6}{l}{All metrics are averaged over 1000 test samples with 95% confidence intervals. Processing time measured on NVIDIA A100 GPU. SSIM: Structural Similarity Index Measure (higher is better). PSNR: Peak Signal-to-Noise Ratio in decibels (higher is better). Memory usage includes model parameters and runtime buffers. Frame rate measured under standard batch processing conditions.} \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._The comparison of attention mechanisms and feature fusion strategies also revealed notable findings. Cross-attention outperformed other mechanisms, achieving the highest accuracy of 94.5% and an SSIM of 0.905, indicating its strength in capturing dependencies across temporal and spatial domains. Adaptive fusion demonstrated the best performance among feature fusion strategies, with an accuracy of 94.8% and a PSNR of 31.0 dB, showcasing its ability to dynamically balance feature contributions. These results collectively emphasize the synergistic importance of temporal and spatial modeling, attention mechanisms, and advanced feature fusion in enhancing the model's predictive performance and structural consistency.