基于ResNet和迁移学习的古印章文本识别

古印章文本因图像退化与超多分类等特点导致识别难度大，部分字符的标注数据不足造成基于深度学习的模型识别准确率不高，泛化能力差。针对上述问题，提出基于深度残差网络（ResNet）和迁移学习的古印章文本识别方法。使用深度残差网络作为特征提取网络，利用人工合成字符样本作为源域进行预训练。将自建古印章文本识别数据集作为目标域，引入迁移学习并结合数据增强和标签平滑策略建立分类模型。最后，对比多种网络下的识别结果并验证迁移学习有效性。结果表明，该方法可以有效提升识别准确率。

Historical Chinese Seal Text Recognition Based on ResNet and Transfer Learning

It is difficult to recognize historical Chinese seal text due to image degradation and super classification. In addition, the insufficient annotation data lead to poor generalization ability and classification accuracy. According to the above problems, a historical Chinese seal text recognition method based on ResNet and transfer learning is proposed. Firstly, using synthetic dataset as source domain, a pre-trained model is trained on deep residual network. Secondly, transfer learning is introduced to model combining data enhancement and label smoothing. In this process, the historical Chinese seal text dataset is taken as target domain. Finally, the recognition results in different networks are compared and the transfer learning effectiveness is analyzed. The experimental results show that this method can improve recognition accuracy effectively.