# Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy
###### Abstract  
Traditional methods of assessing handwritten characters are often too subjective, inefficient, and lagging in feedback, which makes it difficult for educators to achieve fully objective writing assessments and for writers to receive timely suggestions for improvement. In this paper, we propose a convolutional neural network (CNN) architecture that combines the attention mechanism with multi-scale feature fusion; specifically, the features are weighted by designing a bottleneck layer that combines the Squeeze-and-Excitation (SE) attention mechanism to highlight the important information and by applying a multi-scale feature fusion method to enable the network to capture both the global structure and the local details of Chinese characters. Finally, a high-quality dataset containing 26,800 images of handwritten Chinese characters is constructed based on the application scenario of the writing grade test, covering the common Chinese characters in the writing grade exam; The experimental results show that the proposed method achieves 98.6% accuracy on the writing grade exam dataset and 97.05% on the ICDAR-2013 public dataset, significantly improving recognition accuracy. The constructed dataset and improved model are suitable for application scenarios such as writing grade exams, which helps to improve marking efficiency and accuracy. writing grade exam; offline handwritten Chinese character recognition; attention mechanism; multi-scale feature fusion Article  
10 December 202418 January 202518 January 2  
process writing samples, which not only improves grading efficiency but also reduces subjective bias. The application of Handwritten Chinese Character Recognition (HCCR) in writing grade examinations has an important research background and practical significance. With the development of information technology and the gradual electronification of paper writing, handwritten Chinese character recognition, as a key technology, has become a research hotspot in both academia and industry. Handwritten Chinese character recognition faces multiple challenges, especially the difficulty in dealing with Chinese characters, a complex character system. The complexity of Chinese characters is mainly manifested in their large number, complex character structures, and diverse writing styles. There are thousands of commonly used characters and tens of thousands of less commonly used characters. At the same time, there are many similar characters with small differences, which further increases the difficulty of recognition [1]. Therefore, improving the accuracy of handwritten Chinese character recognition has become a research priority. Particularly, with the remarkable advancements of convolutional neural networks (CNNs) in the field of image recognition, such as capturing multi-scale features through multi-scale convolutional kernels (e.g., Inception modules [2]) or feature pyramid networks (FPN [3]). Additionally, attention mechanisms like Squeeze-and-Excitation (SE) [4] and Convolutional Block Attention Module (CBAM) [5] have significantly enhanced the model's ability to focus on key features by adaptively adjusting feature weights. The rapid development of Convolutional Neural Networks (CNNs) has significantly advanced the field of Handwritten Chinese Character Recognition (HCCR). Compared to traditional methods, CNNs eliminate the reliance on subjective feature comparisons (e.g., stroke and radical similarity [6]), greatly improving recognition efficiency. In the context of handwriting grading examinations, CNNs can effectively reduce misjudgments caused by the subjective biases of human evaluators. Moreover, CNNs exhibit greater adaptability to diverse writing styles and are more capable of capturing subtle features, thereby achieving higher recognition accuracy. In recent years, significant advancements have been made in improving the accuracy of Chinese character recognition models.