Inspired by the theory of Leitners learning box from the field of psychology,
we propose DropSample, a new method for training deep convolutional neural
networks (DCNNs), and apply it to large-scale online handwritten Chinese
character recognition (HCCR). According to the principle of DropSample, each
training sample is associated with a quota function that is dynamically
adjusted on the basis of the classification confidence given by the DCNN
softmax output. After a learning iteration, samples with low confidence will
have a higher probability of being selected as training data in the next
iteration; in contrast, well-trained and well-recognized samples with very high
confidence will have a lower probability of being involved in the next training
iteration and can be gradually eliminated. As a result, the learning process
becomes more efficient as it progresses. Furthermore, we investigate the use of
domain-specific knowledge to enhance the performance of DCNN by adding a domain
knowledge layer before the traditional CNN. By adopting DropSample together
with different types of domain-specific knowledge, the accuracy of HCCR can be
improved efficiently. Experiments on the CASIA-OLHDWB 1.0, CASIA-OLHWDB 1.1,
and ICDAR 2013 online HCCR competition datasets yield outstanding recognition
rates of 97.33%, 97.06%, and 97.51% respectively, all of which are
significantly better than the previous best results reported in the literature.

本研究提出了一个新的基于 Leitner 学习盒原理的 Deep Convolutional Neural Networks 的训练方法，名为 DropSample，并将其应用于大规模在线手写汉字识别，最终实验结果表明，这种方法可以显著提高识别准确率。

DropSample：一种增强大规模无约束手写汉字识别深度卷积神经网络的新训练方法

DropSample: A New Training Method to Enhance Deep Convolutional Neural  Networks for Large-Scale Unconstrained Handwritten Chinese Character  Recognition