# Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU
## 2 Related Work
[39] used variational autoencoders to encode latent image variables and predict dense pixel trajectories to infer potential object motion in a scene. For instance, the authors of [40] introduced an innovative prediction technique that captures subtle behavioural cues, suggesting future actions and representinghuman motion across various levels of granularity. Meanwhile, a \"bag-of-words\" approach was presented that models object activity using histograms of spatiotemporal features to capture temporal changes [41]. [PERSON]. [42] proposed a database-driven technique for anomaly detection in video sequences, enabling the evaluation of non-target events and the prediction of future occurrences. Similarly, [PERSON]. [43] introduced the uncertain hypothesis test to assess the suitability of uncertain regression models. They further explored the uncertain significance test to determine whether certain pre-specified regression coefficients within an uncertain regression model can be considered zero [44; 45]. As a result, deep learning-based predictive models primarily rely on maximum likelihood estimation to construct predictive frameworks and define associated loss functions [46; 47]. In a separate study, the authors of [14] introduced the dynamic multi-scale voxel flow network (DMVFN), a groundbreaking model featuring a differentiable routing module capable of perceiving motion scales within video frames. After training, the model adapts during inference by selecting optimal subnetworks based on input features. By utilising only RGB images, DMVFN minimises computational cost while delivering superior video prediction performance compared to previous methods. Notably, DMVFN's applicability extends to tasks involving video sequences of handwritten digits, where accurate motion prediction and feature extraction are essential for identifying character trajectories over time. Furthermore, [PERSON]. [48; 49; 50; 51] made significant advancements in uncertain regression models by proposing an innovative hypothesis testing framework to evaluate their suitability. They also introduced the uncertain significance test, which allows researchers to determine whether specific regression coefficients within these models can be considered null. These methods have shown promising outcomes in scenarios requiring robust predictions, including video sequences featuring handwritten digits, where uncertainty quantification plays a crucial role in ensuring model reliability [52]. Traditionally, deep learning predictive frameworks have relied heavily on maximum likelihood estimation (MLE) as the foundation for defining loss functions. Although MLE can improve accuracy, it may sacrifice flexibility in capturing complex, non-linear relationships within the data. Researchers are now exploring alternative approaches, such as Bayesian methods, ensemble models, or hybrid loss functions, which aim to strike a balance between accuracy, interpretability, and generalisation capability. This evolution is particularly relevant for tasks involving handwritten digits in the video sequences, where capturing subtle spatial-temporal patterns is pivotal. Deep learning has evolved toward end-to-end trainable networks, which are designed to autonomously forecast future sequences without strict reliance on MLE principles [53]. To enhance conventional deep learning-based video prediction models, [PERSON]. [54] proposed the combination of involution and convolution operators (CICO). This model addresses issues such as insufficient spatial feature extraction and lower prediction accuracy. Its design includes multi-scale convolution kernels for robust spatial feature extraction, an involution operator to replace larger convolution kernels for improved computational efficiency, and a \(1\times 1\) convolution kernel for linear mapping to foster integration among diverse features.