  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.
