# Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy
###### Abstract
The ResNet-Centerloss [7] model achieved an accuracy of 97.03%, setting a new benchmark; the improved SqueezeNet [8], in 2021, achieved 96.32% accuracy while reducing the model size; and the CUE model combining the head self-information [9], in 2023, also achieved 96.96%. In 2024, the improved VGG-net, combined with a self-constructed dataset, achieved 88.3% accuracy in style recognition and 95.6% in character recognition [10]. In the same year, this study proposed a CNN-based Chinese recognition method combining HOG feature extraction, Euclidean distance analysis, and the Google LeNet Inception-v3 model, achieving a 93.12% recognition rate [11]. Although deep learning-based HCCR techniques have made significant progress in accuracy, they still face several challenges. Firstly, Chinese characters have complex glyph structures and many similar characters, and the differences between these similar characters are usually very small [1]. Secondly, in writing grade tests, simple characters are more predominant, and the feature scales of these simple characters differ significantly from those of complex characters. Therefore, the size of the convolution kernel needs to be optimized in the network design to accommodate the feature extraction needs of different types of Chinese characters. A larger convolutional kernel can increase the sensing field and better acquire the global information of complex shape approximation characters [12], while a smaller convolutional kernel is suitable for extracting the detailed features of simple characters, thus reducing the amount of unnecessary computation and avoiding the problem of over-computation of simple characters or under-extraction of features of complex characters. In addition, in deep convolutional networks, the back layer is mainly responsible for extracting deep abstract features, but to some ex tent, it is insufficiently combined with the low-level features of the front layer, which leads to insufficient ability to capture certain detailed features [13]. For this reason, the introduction of an attention mechanism [14] is expected to enhance the network's ability to jointly capture global and local information, thus better supporting complex recognition tasks [15]. Furthermore, significant advancements have been made in large-scale character recognition in other countries. For instance, [PERSON] proposed a solution for offline recognition of Japanese characters using minutiae and other features extracted from handwriting images [16]. In Korea, to address the structural complexity of Hangul, [PERSON] conducted a comprehensive study using three neural network architectures -- Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) -- on a self-constructed Handwritten Hangul dataset [17]. In the digitization of ancient Egyptian Hieroglyphs, researchers created a hand-drawn hieroglyph dataset and validated its efficiency for recognition using CNN models like ResNet and VGG [18]. To address the challenges in Urdu handwritten character recognition, researchers developed a Nasta'liq-style dataset and introduced an optimized CNN architecture. The proposed model demonstrated superior performance, achieving higher accuracy compared to existing state-of-the-art systems [19]. In this context, this paper proposes an improved network structure based on CNN, aiming to better handle the multi-scale feature extraction problem in Chinese character recognition. By introducing a multi-scale convolutional kernel and an attention mechanism, this study enables the network to adapt to the small differences in the shape of close characters of Chinese characters, while ensuring efficient recognition of simple characters.