Attentional Mechanisms for Enhancement in the Convolutional Neural Network Task of Recognizing Handwritten Chinese Character Styles

This paper investigates the enhancement of attention mechanism in the task of recognizing handwritten Chinese character styles by convolutional neural networks (CNNs). With the wide application of deep learning in image recognition, convolutional neural network has become a mainstream method for processing image tasks due to its excellent feature extraction capability. However, in complex handwritten Chinese character recognition, the performance of traditional CNN models is still somewhat limited, especially in the task of recognizing different styles of calligraphic glyphs. For this reason, this paper proposes an attention mechanism, that can improve the model's ability to pay attention to important information, thus improving the accuracy of the model in recognizing different handwritten Chinese character patterns. By conducting experiments on the Chinese Calligraphy Styles by Calligraphers dataset, the paper shows that adding an attention mechanism significantly improves the functionality of CNN models on the task of recognizing different fonts. The result demonstrates that the attention mechanism can effectively mitigate the recognition difficulties due to the variation of calligraphic fonts, and provides new ideas for future applications in more complex scenarios.