Optimization of Chinese Character Font Recognition Model Based on Incremental Learning

Chinese character font recognition is a vital research area with applications in text recognition, artistic font design, and handwriting recognition. Traditional methods for this task are time-consuming, labor-intensive, and error-prone. The rise of deep learning has opened new possibilities in this field. However, neural network models often struggle with generalization and robustness, especially with new data, leading to catastrophic forgetting of old data. To address this, we propose an optimization algorithm for the Chinese character font recognition model SwordNet, combining parameter isolation and regularization. We designed an incremental module for SwordNet, enhancing it for better performance and scalability. The loss function incorporates a regularization term based on knowledge distillation principles, limiting significant parameter changes for old data and ensuring the model retains knowledge while learning new data. To tackle the diversity of Chinese character fonts and aid model recognition, we introduced a data augmentation method using noise, rotation, and occlusion. Experiments demonstrate that our regularized incremental learning optimization algorithm achieves a 98.81% accuracy rate with only new data, comparable to models trained on both new and old datasets. Notably, it also reduces training time by approximately 30%, marking a significant improvement in efficiency and accuracy for Chinese character font recognition.