The　meter　reading　with　machine　vision　greatly　improves　the　efficiency　of　industrial　monitoring.　However,　the　pointer　and　scales　of　the　meter　can　be　occluded　by　rain　or　dirt,　which　greatly　reduces　the　accuracy　of　the　meter　reading　recognition.　To　solve　this　problem,　we　propose　a　generative　adversarial　network　(PMS-GAN)　with　pointer　generation　and　main　scale　detection　for　occluded　meter　reading.　Specifically,　dilated　convolution　block　is　designed　to　correlate　separated　pointer　features.　Then　multi-scale　feature　fusion　mechanism　is　proposed　to　guarantee　the　precision　of　pointer　generation　and　main　scale　detection　with　guidance　of　semantic　information.　Moreover,　feature　enhancement　mechanism　is　proposed　to　construct　the　long-range　relationship　for　generating　pointer　under　high　occlusion.　Finally,　the　reading　is　accomplished　by　calculating　local　angle　with　generated　pointer　and　detected　main　scales.　Experiments　show　that　PMS-GAN　can　generate　more　intact　pointer　and　detect　main　scales　to　guarantee　the　success　and　accuracy　of　occluded　meter　reading.　©　2024　Elsevier　Ltd