# Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
## 5 Conclusions  
In this paper, the proposed ConvGRU architecture successfully addresses the fundamental challenges in handwritten digit video prediction through several key innovations. First, we introduced a modified gating mechanism specifically optimized for handwritten content, which significantly improves the model's ability to capture subtle variations in writing styles and stroke patterns. Second, we involve the development of a novel feature decoupling strategy. The decoupling mechanism proves particularly effective in handling complex backgrounds and varying lighting conditions, maintaining robust performance even under challenging environmental circumstances. Finally, we achieve the implementation of an adaptive attention mechanism for feature integration. This mechanism dynamically adjusts its focus based on the importance of different spatial and temporal features, resulting in more accurate predictions across diverse writing styles and multiple digit sequences. Our comprehensive experimental results and theoretical analysis demonstrate several substantial contributions to the field of computer vision and pattern recognition. Despite the significant achievements, our current work has several limitations that warrant further investigation. First, the system's performance shows some degradation when \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **SSIM** & **PSNR (dB)** & **Time (ms)** \\ \hline Full model & 95.3 & 0.913 & 31.2 & 15.2 \\ w/o temporal & 91.2 & 0.875 & 29.4 & 12.8 \\ w/o spatial branch & 90.8 & 0.869 & 29.1 & 12.5 \\ w/o attention & 92.4 & 0.888 & 30.2 & 14.1 \\ w/o feature fusion & 89.7 & 0.862 & 28.8 & 13.6 \\ Basic ConvGRU & 88.5 & 0.854 & 28.3 & 11.9 \\ \hline
**Attention Mechanism Variants** & & & & \\ \hline Channel attention & 93.1 & 0.892 & 30.5 & 14.5 \\ Spatial attention only & 92.8 & 0.889 & 30.3 & 14.3 \\ Self-attention & 94.2 & 0.901 & 30.8 & 15.8 \\ Cross-attention & 94.5 & 0.905 & 30.9 & 16.1 \\ \hline
**Feature Fusion Strategies** & & & & \\ \hline Concatenation & 93.8 & 0.898 & 30.6 & 14.8 \\ Addition & 92.5 & 0.885 & 30.1 & 14.2 \\ Weighted sum & 93.2 & 0.891 & 30.4 & 14.6 \\ Adaptive fusion & 94.8 & 0.908 & 31.0 & 15.5 \\ \hline \hline \end{tabular} All experiments conducted with identical training conditions. Results averaged over 5 independent runs. Time measurements include both forward and backward passes.  
\end{table} 
Table 3: Ablation study on model components. processing extremely cursive handwriting, particularly when digits are highly connected or stylized. Second, the current implementation requires GPU acceleration for optimal real-time performance, which may limit deployment options in some scenarios. Finally, future work should explore the integration of statistical language models to improve recognition accuracy in context-rich scenarios. This integration could potentially resolve ambiguities in digit recognition by considering the semantic context of the written sequence. Conceptualization, Y.W., W.K. and H.S. ; methodology, Y.W., W.K. and H.S. ; software, W.K. and H.S. ; validation, Y.W., W.K. and H.S. ; formal analysis, Y.W., W.K. and H.S. ; investigation, Y.W. ; resources, W.K. and H.S. ; data curation, Y.W., W.K. and H.S. ; writing--original draft preparation, Y.W., W.K. and H.S. ; writing--review and editing, Y.W., W.K. and H.S. ; visualization, Y.W., W.K. and H.S. ; supervision, W.K. and H.S. ; project administration, W.K. and H.S. ; funding acquisition, W.K. and H.S. All authors have read and agreed to the published version of the manuscript. This work is partially supported by the National Key R&D Program of China (No. 2019 YFB21 01600), the National Natural Science Foundation of China (No. 61872025), the Open Fund of the State Key Laboratory of Software Development Environment (No. SKLSDE-2021 ZX-03), and Macao Polytechnic University (File No. RP/FCA-06/2023, fca.eddc.ed61.8). Not applicable.