The　paper　presents　a　Traffic　Sign　Recognition　(TSR)　system,　which　can　fast　and　accurately　recognize　traffic　signs　of　different　sizes　in　images.　The　system　consists　of　two　well-designed　Convolutional　Neural　Networks　(CNNs),　one　for　region　proposals　of　traffic　signs　and　one　for　classification　of　each　region.　In　the　proposal　CNN,　a　Fully　Convolutional　Network　(FCN)　with　a　dual　multi-scale　architecture　is　proposed　to　achieve　scale　invariant　detection.　In　training　the　proposal　network,　a　modified　Online　Hard　Example　Mining　(OHEM)　scheme　is　adopted　to　suppress　false　positives.　The　classification　network　fuses　multi-scale　features　as　representation　and　adopts　an　Inception　module　for　efficiency.　We　evaluate　the　proposed　TSR　system　and　its　components　with　extensive　experiments.　Our　method　obtains　99.88%　precision　and　96.61%　recall　on　the　Swedish　Traffic　Signs　Dataset　(STSD),　higher　than　state-of-the-art　methods.　Besides,　our　system　is　faster　and　more　lightweight　than　state-of-the-art　deep　learning　networks　for　traffic　sign　recognition.　©　2017.　The　copyright　of　this　document　resides　with　its　authors.