Scene　text　detection　is　a　challenging　task　for　the　text-based　information　extraction　systems.　We　present　a　novel　scene　text　detection　method　for　this　task.　The　images　are　over-segmented　into　meaningful　perceptron　superpixels,　and　candidate　connected　components　(CCs)are　extracted　by　combining　local　contrast　and　color　consistency.　The　non-text　components　are　then　pruned　by　a　hierarchical　model　consisting　of　three　stages　in　cascade.　Experimental　results　show　that　our　approach　is　better　than　other　state-of-the-art　methods.