# Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
## 1 Introduction  
The rapid advancement of deep learning technologies has revolutionized the field of video prediction [1], particularly in specialized domains such as handwritten digit sequence analysis [2], while significant progress has been made in general video prediction tasks, the specific challenges posed by handwritten digit sequences demand specializedsolutions that can effectively capture both spatial and temporal characteristics unique to handwriting [3]. Handwritten digit video prediction [4] represents a complex challenge at the intersection of computer vision and pattern recognition. The dynamic nature of handwriting, combined with the need to maintain digit legibility and style consistency across predicted frames, creates unique technical hurdles that conventional video prediction approaches struggle to address effectively. In practical applications [5], such as digital education platforms and real-time handwriting recognition systems, the ability to accurately predict the trajectory and transformation of handwritten digits [6] becomes crucial for system performance. The complexity of handwritten digit video prediction stems from several fundamental challenges [7; 8; 9; 10]. First, the temporal coherence of the appearance of the digits must be maintained across frames while preserving the distinctive characteristics of individual writing styles. Second, when multiple digits are present in the sequence, the system must effectively handle their interactions and potential overlaps without compromising the integrity of each character. Third, the prediction model must operate efficiently enough to meet the real-time processing requirements of practical applications while maintaining high accuracy. Current state-of-the-art video prediction models, such as SimVP [11] and DMVFN [12], have demonstrated impressive capabilities in general video prediction tasks. However, when applied to handwritten digit sequences, these approaches reveal several limitations. Generic models often struggle to preserve digit-specific features throughout the prediction sequence, leading to degradation in character recognition accuracy over time. Additionally, these models frequently exhibit temporal inconsistency in maintaining writing styles and require excessive computational resources for what is essentially a specialized prediction task. To address these limitations, we propose a novel video prediction framework based on Convolutional Gated Recurrent Units (ConvGRU) that specifically targets the challenges of handwritten digit sequence prediction. Our framework introduces several key innovations that significantly advance the state of the art in this domain. First, we develop an enhanced temporal modeling approach through a modified GRU architecture optimized for digit sequences, incorporating specialized gating mechanisms that improve feature preservation across frames. Second, we implement an efficient feature decoupling mechanism that separately processes background and foreground information, reducing parameter complexity while maintaining high prediction accuracy. The main contributions can be summarize as follows:  
* Feature extraction and decoupling model: By incorporating an improved Generative Adversarial Network (GAN), a novel decoupling model is constructed. This model consists of two feature extractors that separate the input information into background and foreground features. This approach enables the prediction process to focus on the motion within the sequence, optimizing parameter selection for the digit position prediction network, reducing training time, and improving efficiency. * Temporal feature learning: To address the insufficient learning of temporal features in digit position prediction, this paper integrates GRU into the position prediction network. GRU automatically captures long-term temporal dependencies between data points and, compared to Long Short-Term Memory (LSTM) networks, significantly reduces the number of parameters.