As　an　important　step　in　text-based　information　extraction　systems,　scene　text　detection　has　become　a　popular　subject　of　research　in　recent　years.　In　this　study,　the　authors　present　a　novel　approach　to　robustly　detect　texts　which　are　variable　in　scales,　colours,　fonts,　languages　and　orientations　in　scene　images.　To　segment　candidate　text　connected　components　(CCs)　from　images,　both　local　contrast　and　colour　consistency　are　considered　in　superpixel　level.　To　filter　out　the　non-text　CCs,　a　hierarchical　model　is　designed.　This　hierarchical　model　groups　the　CCs　into　three　cascaded　stages,　and　is　equipped　with　a　well-designed　classifier　in　each　stage.　Experimental　results　on　the　public　ICDAR　2005　dataset　and　the　MSRA-TD500　dataset　show　that　their　approach　obtains　better　performance　than　other　state-of-the-art　methods.