一种基于深度学习的古彝文识别方法

作为世界六大古文字之一的古彝文记录了几千年来人类的发展历史。通过对古彝文的识别能够将这些珍贵文献资料转换为电子文档，便于保存和传播。由于历史发展、区域限制等原因，针对古彝文识别的研究鲜有成果。本文将当前新颖的深度学习技术应用于古老的文字识别。在四层卷积神经网络（convolutional neural network, CNN）基础上扩展出5个模型，然后利用Alpha-Beta散度作为惩罚项,对5个模型的输出神经元重新进行自编码，接着用2个全连接层完成特征压缩，最后在softmax层对古彝文字符特征进行重新评分，得到其概率分布，选择对应的最高概率作为识别的字符。实验表明，相对于传统CNN模型，本文方法对古彝文手写体的识别精度更高。

A recognition method of Ancient Yi character based on deep learning

Ancient Yi is one of six kinds of ancient word in the world and it recorded human development history in thousands of years. The recognition technology of Ancient Yi character will enable us to transform lots of precious Ancient Yi literature into electronic documents which is convenient for storage and spread. Due to imbalance of historical development and territorial limitation, the research on recognition of ancient Yi is rare. In this article, we apply novel deep learning to ancient character recognition. Our framework consists of five models which are extended based on four-layer convolutional neural network (CNN).Then we take Alpha-Beta divergence as penalty term to implement the coding for output neurons of five models. Next, two fully connected layers finish characteristics compression. Finally, we use softmax layer to reevaluate characteristics of ancient Yi character and get the probability distribution. The character owning the highest probability is identified as the target character. Experiments show that our method has higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.