In　this　paper,　we　investigate　the　performance　of　super-resolution　restoration　(SR)　algorithms　for　the　screen　content　images　(SCIs),　which　emerged　with　the　rapid　development　of　multi-device　communication.　SR　is　a　crucial　technique　to　improve　the　perceptual-quality　of　low-resolution　SCIs　captured　from　low-cost　imaging　sensors.　However,　most　of　existing　SR　algorithms　are　designed　for　natural　scene　images　(NSIs),　and　their　performance　for　SCIs　is　rarely　studied.　In　order　to　verify　the　effectiveness　of　the　existing　NSIs-oriented　SR　algorithms　on　SCIs,　we　select　eight　classical　or　advanced　SR　algorithms　as　representative.　Then,　their　performance　for　SCIs　is　measured　by　nine　state-of-the-art　image　quality　assessment　(IQA)　metrics　which　have　been　proven　to　be　consistent　with　human　perception.　Finally,　we　can　verify　the　effectiveness　of　those　SR　algorithms　on　SCIs　by　comparing　the　objective　quality　scores　derived　from　those　IQA　metrics.