论文标题
全球常规网络作者身份证
Global Regular Network for Writer Identification
论文作者
论文摘要
作者鉴定在伪造检测和法医科学方面具有实际应用。大多数基于深度神经网络的模型从字符图像或字符图像中的子区域提取特征，这些特征忽略了页面区域图像中包含的特征。我们提出的全球常规网络（GRN）会注意这些功能。 GRN网络由两个分支组成：一个分支将页面手写作为输入以提取全局功能，而另一个分支将单词笔迹作为输入来提取本地功能。全球功能和本地功能以一种全球剩余方式合并，以形成笔迹的整体特征。提出的GRN有两个属性：一个是在页面中包含的提取功能中添加一个分支；另一个正在使用残留的注意网络提取本地功能。实验证明了这两种策略的有效性。在CVL数据集上，我们的模型实现了令人印象深刻的99.98％TOP-1准确性和100％的前5个精度，较短的训练时间和更少的网络参数，超过了最先进的结构。该实验显示了网络在作者识别领域的强大能力。源代码可从https://github.com/wangshiyu001/grn获得。
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.
