Optical character recognition (OCR) has been widely applied in the computer vision, but the text recognition (TR) in the complex text scenes still faces the challenge of low TR accuracies. Therefore, a contrastive learning (CL)-based multi-scale attention (CLMSA)model for TR is proposed, aiming to improve the TR ability of TR model in the complex text scenes. Firstly, the modality transition module is improved by introducing the multi-scale attention mechanism to capture the global and local features in the image to enhance the ability of model to understand the complex text content. Secondly, a data augmentation-based invariant feature learning (DAIFL) strategy is studied to construct the enhancement domain by data augmentation and optimize the modality transition module based on the multiple-kernel maximum mean discrepancy (MK-MMD) to improve the robustness of the model. Thirdly, a character similarity-based contrastive loss (CSCL) is designed to construct the sample pairs by calculating the character similarities between different images to improve the TR effect of the model in the noisy scenes. Finally, a series of experiments are carried out to verify the effectiveness of the proposed model. The results reveal that the proposed CLMSA model can obtain excellent TRperformances in the complex text scenes.