# Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU
## 1 Introduction  
The rapid development of deep learning technologies has had a significant impact on the field of video prediction [1, 2], particularly in specialised areas such as the analysis of handwritten digit sequences [3]. In fact, the unique challenges of handwritten digit sequences require tailored solutions to effectively capture both spatial and temporal features specific to handwriting, while significant progress has been made in general video prediction tasks [4]. Video prediction of handwritten digits is a complex challenge at the intersection of computer vision and pattern recognition [5, 6]. The inherently dynamic nature of handwriting, which presents fluid and ever-changing forms, presents a unique set of technical challenges for video prediction systems. These systems must capture the unpredictable movement and variation inherent in handwriting while ensuring that the predicted frames maintain the clarity of the written character and stylistic consistencythroughout the sequence. Achieving this balance is essential as even small deviations in either clarity or style can compromise the usability and reliability of the prediction results. In practical applications such as digital learning platforms and real-time handwriting recognition systems, accurate prediction of the trajectory and transformation of handwritten digits is essential to improve system performance [7; 8]. It is evident that conventional video prediction methods often lack the sophisticated mechanisms required to effectively address these intertwined challenges, highlighting the need for more specialised approaches tailored to the intricacies of handwriting dynamics. Furthermore, the prediction of handwritten digit video sequences poses numerous challenges [9; 10]. Firstly, it is necessary to preserve the temporal continuity of digit appearances across consecutive frames while maintaining the unique traits of individual writing styles. Furthermore, in sequences comprising multiple digits, the model must effectively manage their interactions and potential overlaps, thereby ensuring the integrity of each character is preserved. Finally, the prediction system must operate efficiently to fulfil real-time processing demands in practical applications, while achieving high levels of accuracy [11]. Although state-of-the-art video prediction models such as SimVP [12], SimVPv2 [13], and DMVFN [14] have demonstrated exceptional performance in general video prediction tasks, they encounter limitations when applied to handwritten digit sequences. These generic models frequently fail to preserve the distinctive features of digits throughout the prediction sequence, resulting in a decline in character recognition accuracy [15]. Also, these models face challenges in preserving temporal consistency in writing style and require substantial computational resources for a task that is inherently specialised [16; 17]. To improve performance, an advanced video prediction framework using convolutional content-adaptive recurrent units (ConvCARUs) was introduced, specifically targeting the prediction of handwritten digit sequences. This framework incorporates several key innovations that represent significant advances in the field. The primary innovation is an improved temporal modelling approach, achieved by modifying the CARU architecture [18], DCGAN [19], and SimVP [12]; these approaches still struggle to adequately predict the dynamic behaviour of handwritten digits. The inherent challenge lies in accurately capturing the intricate spatiotemporal dependencies while preserving fine structural details over long prediction horizons. This paper aims to overcome these challenges by introducing an enhanced prediction framework based on a Convolutional content-adaptive recurrent unit (ConvCARU) integrated with a modified GAN.