Scene　text　image　super-resolution　(STISR)　aims　to　enhance　the　resolution　and　legibility　of　text　in　low-resolution　images.　In　cases　of　spatial　deformation　or　low-resolution　text　images,　the　lack　of　details　in　text　regions　and　the　difficulty　in　aligning　semantic　cues　and　visual　features　with　character　position　make　it　difficult　to　recognize　text　effectively.　In　order　to　address　these　challenges,　this　paper　proposes　a　perceiving　multi-domain　character　distance　for　scene　text　image　super-resolution　method　(PMDC),　which　improves　the　image　text　region　and　edge　texture　details.　Firsly,　the　visual　and　semantic　features　are　extracted　by　using　the　asymmetric　convolution　module　along　with　the　semantic　prior　module.　Then　the　enhanced　position　coding　is　obtained　by　the　character　distance　perception　module　to　perceive　the　distance　change　and　semantic　similarity　between　characters.　Finally,　the　guiding　cues　and　visual　features　are　combined　to　restructure　the　pixels　and　generate　a　super-resolution　text　image.　In　comparison　to　TATT,　experimental　results　from　the　public　dataset　TextZoom　showed　an　increase　of　0.11　dB　in　the　fidelity　of　the　peak　signal-to-noise　ratio　index.　This　improvement　effectively　enhances　the　clarity　of　the　text　area　and　the　detailed　edge　texture.　Additionally,　the　recognition　accuracy　was　improved　by　1.4%,　which　effectively　enhances　the　readability　of　the　text　image.　©　2024　Chinese　Institute　of　Electronics.　All　rights　reserved.