The segmentation-free research efforts for addressing handwritten text recognition can be divided into three categories: connectionist temporal classification (CTC), hidden Markov model and encoder-decoder methods. In this paper, inspired by the above three modeling methods, we propose a new recognition network by using a novel three-dimensional (3D) attention module and global-local context information. Based on the feature maps of the last convolutional layer, a series of 3D blocks with different resolutions are split. Then, these 3D blocks are fed into the 3D attention module to generate sequential visual features. Finally, by integrating the visual features and the corresponding global-local context features, a well-designed representation can be obtained. Main canonical neural units including attention mechanisms, fully-connected layer, recurrent unit and convolutional layer are efficiently organized into a network and can be jointly trained by the CTC loss and the cross-entropy loss. Experiments on the latest Chinese handwritten text datasets (the SCUT-HCCDoc and the SCUT-EPT) and one English handwritten text dataset (the IAM) show that the proposed method can make a new milestone.

本研究针对手写文本识别中的分割问题，提出了一种新颖的基于三维注意力模块和全局-局部上下文信息的识别网络。实验结果表明，该方法在最新的中文与英文手写文本数据集上取得了显著进展，标志着该领域的新里程碑。

结合典型神经单元与多尺度训练的手写文本识别