This paper presents an end-to-end neural network system to identify writers through handwritten word images, which jointly integrates global-context information and a sequence of local fragment-based features. The global-context information is extracted from the tail of the neural network by a global average pooling step. The sequence of local and fragment-based features is extracted from a low-level deep feature map which contains subtle information about the handwriting style. The spatial relationship between the sequence of fragments is modeled by the recurrent neural network (RNN) to strengthen the discriminative ability of the local fragment features. We leverage the complementary information between the global-context and local fragments, resulting in the proposed global-context residual recurrent neural network (GR-RNN) method. The proposed method is evaluated on four public data sets and experimental results demonstrate that it can provide state-of-the-art performance. In addition, the neural networks trained on gray-scale images provide better results than neural networks trained on binarized and contour images, indicating that texture information plays an important role for writer identification. The source code is available: https://github.com/shengfly/writer-identification.

本文提出了一种通过手写文字图像识别作者的端到端神经网络系统，该系统将全局上下文信息和一系列基于局部片段的特征联合在一起。通过全局平均池化步骤从神经网络的尾部提取全局上下文信息。局部和基于片段的特征的序列是从一个低层的深层特征图提取的，该图包含有关笔迹样式的细微信息。片段序列之间的空间关系通过递归神经网络（RNN）建模，以增强局部片段特征的判别能力。我们利用全局上下文和局部片段之间的补充信息，从而提出了全局上下文残差递归神经网络（GR-RNN）方法。该方法在四个公共数据集上进行了评估，实验结果表明该方法可以提供最新的性能。另外，在灰度图像上训练的神经网络比在二值化和轮廓图像上训练的神经网络提供更好的结果，这表明纹理信息对于作者识别起着重要的作用。源代码可用：https：//github.com/shengfly/writer-identification。