The existing incomplete text image restoration must identify and fill the Mask region. The Mask region cannot be determined if the residual information of the incomplete text part is too sparse. A blind restoration method of incomplete and sparse text images based on content style transfer is proposed to address this issue. Using a cyclic generative adversarial network to construct the global related pixel information between the text images before and after restoration, the image content style features of the incomplete text were transferred to complete text images for restoration. The selfattention mechanism is added to the network to globally restrict the sparse pixels, thereby resolving the issue of weak dependence between the text sparse pixels far away in the migration process. simultaneously, the maximum pooling is used in the selfattention mechanism to enhance the texture features of the text images after migration and restoration. To improve migration accuracy, the least square loss function is used to replace the sigmoid cross-entropy loss function in the original network model. The proposed algorithm can repair random unknown missing regions in sparse incomplete text images without the assistance of Mask, according to experimental results.