Abstract: Existing image inpainting methods based on deep learning mostly require pair inputs of damaged image and binary mask. However, mask image of damaged image is not available in practice. In this work, a blind inpainting method based on contextual semantic recursive reasoning is proposed. The whole framework comprises of two modules named as local padding network (LPN) and detail refining network (DRN). Under the guidance of local contextual semantic, dirtied regions in image is estimated automatically and coarsely filled by LPN. Then image patches of inconsistent semantic in coarse image resulted from LPN is fixed by DRN, which uses multi-scale feature of local context, and a clear and natural image is obtained consequently. Extensive experiments show that the presented algorithm performs surpass the counterparts and can produces clear images of higher global semantic consistency.