Given　a　corrupted　image,　image　inpainting　aims　to　complete　the　image　and　outputs　a　plausible　result.　When　we　complete　the　missing　region,　we　always　borrow　the　information　from　a　known　area,　which　is　aimless　and　causes　unsatisfactory　results.　In　our　daily　life,　some　other　information　is　often　used　for　corrupted　images,　such　as　text　descriptions.　Therefore,　we　introduce　the　use　of　text　information　to　guide　image　inpainting.　To　fulfill　this　task,　We　introduce　an　inpainting　model　named　TGNet　(Text-Guided　Inpainting　Network).　We　provide　a　text-image　gated　feature　fusion　module　to　fuse　text　feature　and　image　feature　deeply.　A　mask　attention　module　is　proposed　to　enhance　the　consistency　of　known　areas　and　the　repaired　area.　Extensive　quantitative　and　qualitative　experiments　on　three　public　datasets　with　captions　demonstrate　the　effectiveness　of　our　method.　©　2022　IEEE.