Aiming at the difficulty to reconstruct clear high-resolution images from blurred low-resolution images by the existing super-resolution methods, a joint text image joint super-resolution and deblurring method based on Generative Adversarial Network (GAN) was proposed. Firstly, the low-resolution text images with severe blur were focused, and the down-sampling module and the deblurring module were used to generate the generator network. Secondly, the input images were down-sampled by the down-sampling module to generate blurred super-resolution images. Thirdly, the deblurring module was used to reconstruct the clear super-resolution images. Finally, in order to recover the text images better, a joint training loss including super-resolution pixel loss, deblurring pixel loss, semantic layer feature matching loss and adversarial loss was introduced. Extensive experiments on synthetic and real-world images demonstrate that compared with the existing advanced method SCGAN (Single-Class GAN), the proposed method has the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM) and OCR (Optical Character Recognition) accuracy improved by 1.52 dB, 0.011 5 and 13.2 percentage points respectively. The proposed method can better deal with degraded text images in real scenes with low computational cost.