Research and Implementation of Handwritten Chinese Character Recognition Based on Deep Learning Algorithm

In this study, we explore deep learning models for single-character Chinese character recognition tasks, with a special focus on two architectures: VGG19 and EfficientNetV2. By improving recognition accuracy with limited computational resources and dataset size, this study aims to address real-world challenges.In the field of image recognition, deep learning has shown excellent ability, especially the importance of convolutional Neural Network (CNN). This study reviews the history of handwritten Chinese character recognition and explores the application of deep learning in various fields such as object detection, image classification, and semantic segmentation. The research methodology incorporates problem analysis, data collection, model construction, training, and result generation. PyTorch is used as the basic framework to implement the model, and strict data preprocessing is conducted to optimise the performance. The user interface and interaction design allow us to show the practical application of the model and encourage user-friendly participation. By applying VGG19 and EfficientNetV2 models in a single-character Chinese character recognition task, we reveal the impact of limited training data and computational constraints on accuracy and performance. We confirm that higher training cycles improve accuracy, but we also note diminishing returns. Meanwhile, the research highlights the exciting potential of deep learning in character recognition tasks and advocates its widespread application in practice. While overcoming computational and data limitations, our study reveals the intricate relationship between model training, accuracy improvement, and practical usability. The experimental results show that the average training result of the EfficientNetV2 model is 94.957322%, and the average training result of the VGG19 model is 95.756285%. This study provides dedicated support for the in-depth research and development of Chinese character recognition and its various application fields.