Writer identification has practical applications for forgery detection and forensic science. Most models based on deep neural networks extract features from character image or sub-regions in character image, which ignoring features contained in page-region image. Our proposed global regular network (GRN) pays attention to these features. GRN network consists of two branches: one branch takes page handwriting as input to extract global features, and the other takes word handwriting as input to extract local features. Global features and local features merge in a global residual way to form overall features of the handwriting. The proposed GRN has two attributions: one is adding a branch to extract features contained in page; the other is using residual attention network to extract local feature. Experiments demonstrate the effectiveness of both strategies. On CVL dataset, our models achieve impressive 99.98% top-1 accuracy and 100% top-5 accuracy with shorter training time and fewer network parameters, which exceeded the state-of-the-art structure. The experiment shows the powerful ability of the network in the field of writer identification. The source code is available at https://github.com/wangshiyu001/GRN.

提出了一种全局正则网络（GRN），其由一个带有全局特征提取器的分支和一个带有局部特征提取器的分支组成，两者以一种全局残差方式融合，可以有效地识别手写字的写作人，实验表明该模型在CVL数据集上取得了卓越的99.98％-1准确率和100％ top-5准确率，具有更短的训练时间和更少的网络参数。

全球正则网络用于笔迹识别