# Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
## 2 Related Work  
With the rapid proliferation of the internet and advancements in photography equipment, capturing and collecting digital video has become increasingly accessible [13]. This ease of access has provided a diverse range of datasets for the study of video prediction technology. The applications of video prediction are wide-ranging [14; 15; 16; 17; 18; 19], covering areas such as sports, human posture, vehicle movement, and even everyday activities like makeup tutorials. However, the video prediction model [20] explored in this study is still in its early stages, with a particular focus on predicting video frames that include digital posture information. The core of video prediction is forecasting future image sequences based on temporally related images, which makes image generation algorithms highly relevant and insightful for video prediction. The development of variational autoencoders [21] (VAEs) has significantly advanced the field of video prediction. The authors introduced a powerful graph variational autoencoder framework that demonstrated superior performance in capturing complex spatiotemporal dependencies. Building upon this work, the authors enhanced the model's capability through the integration of temporal attention mechanisms, enabling more accurate long-term predictions. GANs [22] optimize convergence through the interaction between the generator and discriminator rather than relying on manually defined loss functions for backpropagation. The discriminator is often a variant of an encoder that provides error signals to guide the generator in producing diverse images. Due to their ability to generate clearer images, GANs have increasingly been integrated into various network models. The authors [23] and their team at OpenAI introduced a flow-based generative network capable of precisely calculating the distribution space of images, allowing users to manipulate images more easily. The application, improvement, and development of deep learning algorithms such as VAEs and GANs in image generation and prediction have laid a solid foundation for their use in video prediction and provided clear research directions. Building on these image generation models, researchers [24] can further explore temporal relationships between images in videos, enhancing the performance of video prediction. Deep learning-based image generation and prediction technologies have become quite advanced [25]. However, video prediction requires not only attention to the details of individual frames but also consideration of the spatial and temporal relationships between adjacent frames. Additionally, prediction models must generate clear image sequences that evolve dynamically over time. Therefore, while image generation algorithms cannot be directly applied to video prediction, they provide significant inspiration for the development of video prediction algorithms. Early video prediction models [11] primarily focused on high-level semantics in videos. For instance, the authors of [26] proposed a new prediction method, suggesting that subtle details in human behavior may indicate future actions, representing human motion at multiple levels of granularity. The authors [27] introduced a \"bag-of-words\" method, modeling object activity as a histogram of spatiotemporal features to capture changes over time. The authors of [28] developed a method based on a large database to identify abnormal events in video clips, assess their abnormality, and predict future events. The authors of [29] proposed a \"maximum-margin\" framework based on structured output SVM for identifying local events and enabling early detection. The authors of [30] extended the \"first principles\" of structured random forest regression to predict the motion trajectories of objects in videos. The authors of [31] used variational autoencoders to encode latent variables from image data, predicting dense pixel trajectories in a scene to infer potential object movements.