Research on writer identification based on attention mechanism

Handwriting recognition, which identifies individuals by analyzing and comparing the features of handwriting samples, is a challenging field of biometric recognition. While deep learning techniques can handle large datasets effectively and provide high recognition accuracy, they still struggle with capturing subtle features in small handwriting datasets. Although previous work has shown that Deep Convolutional Neural Networks (DLS_CNN) perform well on small datasets, there are still shortcomings in extracting fine-grained features of handwriting. Therefore, this paper introduces attention mechanisms into DLS_CNN to enhance the model's ability to capture subtle features. Specifically, two types of attention mechanisms are incorporated into the DLS_CNN. Experimental results demonstrate that adding attention mechanism modules can significantly improve recognition accuracy, with the addition of CBAM attention mechanism being more effective than SE attention mechanism.