In　this　paper,　a　new　scene　text　detection　method　based　on　hierarchical　multilayer　perceptron　(MLP)　is　proposed.　First,　connected　components　(CCs)　are　segmented　locally　by　text　probability　map.　Then,　a　novelty　hierarchical　architecture　consisting　of　two　MLP　classifiers　in　tandem　is　utilized　to　analysis　the　CCs.　In　this　hierarchical　setup,　the　first　stage　MLP　classifier　is　trained　using　unary　property　features.　The　second　stage　MLP　classifier　is　trained　for　CCs　pairs　including　both　posterior　probabilities　estimated　by　first　stage　and　relationship　features.　Finally,　candidate　text　CCs　are　grouping　into　words.　Experimental　results　evaluated　on　the　public　dataset　show　that　our　approach　yields　better　performance　compared　with　state-of-the-art　methods.　©　2011　IEEE.