论文标题
最大熵正则化和中文文本识别
Maximum Entropy Regularization and Chinese Text Recognition
论文作者
论文摘要
由于大量细粒度的汉字和对班级的极大失衡，中文文本识别比拉丁文字更具挑战性，这导致了严重的过度拟合问题。我们建议将最大的熵正则化以使训练过程正规化，即仅在没有任何其他参数和修改模型的情况下向规范的跨透镜损失添加负熵项。从理论上讲，我们给出收敛概率分布，并分析正则化如何影响学习过程。关于中国人物识别，中文文本识别和细粒图像分类的实验可实现一致的改进，证明正则化对识别模型的概括和鲁棒性有益。
Chinese text recognition is more challenging than Latin text due to the large amount of fine-grained Chinese characters and the great imbalance over classes, which causes a serious overfitting problem. We propose to apply Maximum Entropy Regularization to regularize the training process, which is to simply add a negative entropy term to the canonical cross-entropy loss without any additional parameters and modification of a model. We theoretically give the convergence probability distribution and analyze how the regularization influence the learning process. Experiments on Chinese character recognition, Chinese text line recognition and fine-grained image classification achieve consistent improvement, proving that the regularization is beneficial to generalization and robustness of a recognition model.
