Deep learning based methods have been dominating the text recognition tasks in different and multilingual scenarios. The offline handwritten Chinese text recognition (HCTR) is one of the most challenging tasks because it involves thousands of characters, variant writing styles and complex data collection process. Recently, the recurrent-free architectures for text recognition appears to be competitive as its highly parallelism and comparable results. In this paper, we build the models using only the convolutional neural networks and use CTC as the loss function. To reduce the overfitting, we apply dropout after each max-pooling layer and with extreme high rate on the last one before the linear layer. The CASIA-HWDB database is selected to tune and evaluate the proposed models. With the existing text samples as templates, we randomly choose isolated character samples to synthesis more text samples for training. We finally achieve 6.81% character error rate (CER) on the ICDAR 2013 competition set, which is the best published result without language model correction.

本文研究基于深度学习的方法在离线手写中文文本识别领域的应用，使用只有卷积神经网络的模型和CTC损失函数实现，采用Dropout方法以防止过拟合，最终在ICDAR 2013竞赛数据集上实现了6.81%的字符错误率，为同等条件下最好的研究结果。

离线手写中文文本识别与卷积神经网络