Different from traditional image text recognition tasks， the Burmese optical character recognition （OCR） requires computers to recognize complex characters nested and combined by multiple characters in a receptive field， which brings great challenges to Burmese OCR tasks. To solve this problem， a Burmese OCR method based on knowledge distillation is proposed. This paper constructs a model of teacher network and student network using the framework of convolutional neural networks （CNN）+ recurrent neural networks （RNN） to train in an integrated learning way. In the training process， the teacher integrated sub-network is coupled with the student network to realize the alignment of the local character image features corresponding to a single receptive field in the student network and the overall character image features in the teacher network， so as to enhance the acquisition of local features in long sequence character images. The experimental results show that the performance of our model is better than the baseline by 2.9% and 2.7% respectively without and with background noise images as training data sets.