Machine　vision-based　meter　reading　technologies　have　been　developed　to　monitor　the　status　of　devices　in　industrial　sites.　However,　the　integrity　of　the　pointer　can　be　destroyed　by　the　occlusion　on　the　meter,　such　as　dirt　or　rain.　In　this　case,　the　pointer　detection　becomes　a　challenging　task　in　the　meter　reading.　To　overcome　this　challenge,　in　this　article,　the　pointer　generative　adversarial　network　(Pointer-GAN)　is　proposed　for　pointer　mask　generation.　Specifically,　an　occlusion　simulation　method　is　developed　in　the　data　preprocessing　to　provide　sufficient　images　of　occluded　meters　(OMs)　in　the　training　phase,　and　the　dilated　convolution　is　adopted　in　the　residual　block　to　strengthen　the　correlation　among　pointer　features　in　the　long-range.　Next,　the　multiscale　attention　mechanism　(MAM)　is　designed　for　preventing　the　pointer　feature　in　the　low-level　from　being　affected　by　the　noise.　Finally,　the　dense　dilated　convolution　block　(DDC　block)　is　utilized　to　integrate　the　pointer　feature　in　the　low　and　high　level　for　the　pointer　mask　generation.　The　experiments　demonstrate　that　the　Pointer-GAN　can　generate　the　pointer　mask　with　higher　accuracy　for　the　meters　under　occlusion　compared　to　the　other　methods,　thereby　improving　success　rates　of　reading　meters　in　different　occlusion　scenarios.