We　investigate　the　characteristics　of　hard　margin　support　vector　machine　using　a　real-world　handwriting　data.　The　number　of　support　vectors　and　distance　of　margin　are　two　investigated　objectives　in　this　work.　The　structural　risk　of　support　vector　machine　is　influenced　by　the　distance　of　margin　that　is　decided　by　settings　of　the　kernel　function　and　it　parameter.　We　found　that　number　of　support　vectors　are　as　the　same　when　the　parameter　of　kernel　functions　are　changed,　but　the　distance　of　margin　is　highly　influenced　by　the　parameter　of　kernel　functions,　which　is　related　to　the　structural　risk　of　established　support　vector　machine.　The　handwriting　data　are　not　always　mixed　together　in　their　original　representation　space.　Some　of　them　are　linear　separable,　but　some　of　them　are　not.　We　should　design　a　group　of　classifiers　to　handle　this　real-world　handwriting　application　by　considering　these　characteristics　and　the　primary　discoveries　of　this　work.　©　2019,　Springer　Nature　Singapore　Pte　Ltd.