Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU

Video location prediction for handwritten digits presents unique challenges in computer vision due to the complex spatiotemporal dependencies and the need to maintain digit legibility across predicted frames, while existing deep learning-based video prediction models have shown promise, they often struggle with preserving local details and typically achieve clear predictions for only a limited number of frames. In this paper, we present a novel video location prediction model based on Convolutional Gated Recurrent Units (ConvGRU) that specifically addresses these challenges in the context of handwritten digit sequences. Our approach introduces three key innovations. Firstly, we introduce a specialized decoupling model using modified Generative Adversarial Networks (GANs) that effectively separates background and foreground information, significantly improving prediction accuracy. Secondly, we introduce an enhanced ConvGRU architecture that replaces traditional linear operations with convolutional operations in the gating mechanism, substantially reducing spatiotemporal information loss. Finally, we introduce an optimized parameter-tuning strategy that ensures continuous feature transmission while maintaining computational efficiency. Extensive experiments on both the MNIST dataset and custom mobile datasets demonstrate the effectiveness of our approach. Our model achieves a structural similarity index of 0.913 between predicted and actual sequences, surpassing current state-of-the-art methods by 1.2%. Furthermore, we demonstrate superior performance in long-term prediction stability, with consistent accuracy maintained across extended sequences. Notably, our model reduces training time by 9.5% compared to existing approaches while maintaining higher prediction accuracy. These results establish new benchmarks for handwritten digit video prediction and provide practical solutions for real-world applications in digital education, document processing, and real-time handwriting recognition systems.