基于改进inception的脱机手写汉字识别

由于字形的复杂多变，脱机手写汉字的识别一直是模式识别的难题，深度卷积神经网络的发展为其提供了一种直接有效的解决方案。研究基于inceptions 结构神经网络的脱机手写汉字识别，提出了一种inception结构的改进方法，它具有结构更加简单、网络深度扩展更加容易、需要的训练参数量更少的优点。该方法在数据集CISIA-HWDB1.1 上进行了实验验证，采用随机梯度下降优化算法，模型达到了96.95%的平均准确率。实验结果表明，使用改进的inception结构在图像分类上具有更好的鲁棒性，更容易扩展到其他应用领域。

Offline handwritten Chinese character recognition based on improved inception

Due to the complexity and variety of glyphs, offline handwritten Chinese character recognition has always been a difficult problem of pattern recognition. The development of deep convolutional neural networks provides a direct and effective solution to this problem. This paper studied offline handwritten Chinese character recognition based on inceptions neural network. It proposed an improved inception structure, which took the advantages of simpler structure, easier network depth expansion and less training parameters. The method used the proposed structure to verifiy on dataset CISIA-HWDB1.1. The model achieved an average accuracy of 96.95%, by using stochastic gradient descent optimization algorithm. Experimental result shows that the improved inception structure has better generalization performance and robustness in image classification, and can be easily extended to other applications.