面向高性能在线HCCR：一种结合DropDistortion、路径签名和空间随机最大池的CNN方法
Toward high-performance online HCCR: a CNN approach with DropDistortion, path signature and spatial stochastic max-pooling
摘要：
本文研究了提高联机手写汉字识别准确率的几种技术。我们提出了一种新的训练策略，称为DropDistortion训练一个深度卷积神经网络（DCNN）与失真的样本。DropDistortion在训练过程中逐渐降低字符失真的程度，这使得DCNN能够更好地泛化。路径签名用于在线字符的有效特征提取。通过采用空间随机最大池作为特征图失真和模型平均的方法，实现了进一步的改进。实验在三个公开可用的数据集上进行，即CASIA-OLHWDB 1.0，CASIA-OLHWDB 1.1和ICDAR 2013在线HCCR竞赛数据集。所提出的技术产生的最先进的识别准确率分别为97.67%，97.30%和97.99%。
英文摘要：
This paper presents an investigation of several techniques that increase the accuracy of online handwritten Chinese character recognition (HCCR). We propose a new training strategy named DropDistortion to train a deep convolutional neural network (DCNN) with distorted samples. DropDistortion gradually lowers the degree of character distortion during training, which allows the DCNN to better generalize. Path signature is used to extract effective features for online characters. Further improvement is achieved by employing spatial stochastic max-pooling as a method of feature map distortion and model averaging. Experiments were carried out on three publicly available datasets, namely CASIA-OLHWDB 1.0, CASIA-OLHWDB 1.1, and the ICDAR2013 online HCCR competition dataset. The proposed techniques yield state-of-the-art recognition accuracies of 97.67%, 97.30%, and 97.99%, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
