With　the　widespread　use　of　E-paper　technology,　numerous　documents　are　being　digitized　and　displayed　on　E-paper　screens.　However,　the　display　quality　of　degraded　document　images　on　E-paper　often　suffers　from　a　lack　of　detail.　To　address　this　challenge,　we　introduce　a　mapping　model　that　converts　color　images　into　E-paper　display　images.　This　model　leverages　U-Net++　as　its　backbone,　integrating　residual　connectivity　and　dual　attention　modules.　Given　the　presence　of　varying　stroke　thicknesses　in　document　images,　a　fixed-size　convolutional　kernel　is　insufficient.　Therefore,　we　propose　multi-branch　channels　and　spatial　attention　modules　(MCSAM),　which　combines　the　selective　kernel　network　(SKNet)　with　a　spatial　attention　mechanism　to　adaptively　select　the　appropriate　convolutional　kernel　size　based　on　font　size.　To　demonstrate　its　effectiveness,　we　tested　the　mapped　images　on　a　custom　E-paper　display　platform.　Experimental　results　highlight　the　superior　performance　of　our　proposed　method.