Intelligent　inspection　in　the　substation　transformer　using　optical　character　recognizer　has　been　developing　rapidly.　Character　segmentation　from　the　text　line　of　data　plate　is　an　important　step　for　localization　and　recognition　of　electrical　equipment.　However,　on-site　character　segmentation　is　challenging　if　the　data　plate　contains　multiple　languages,　especially　when　the　width　between　Chinese　and　non-Chinese　character　differs　significantly　and　the　complex　environments　cause　the　light　reflection　and　fading.　This　paper　proposes　a　new　method,　based　on　analyzing　the　connected　component　and　Chinese　character＇s　structure,　to　segment　characters　from　multi-language　data　plate　of　substations.　The　proposed　method　uses　the　combination　of　the　HSV　color　space　and　multi-scale　MSRCP　to　reduce　the　effect　of　illumination　and　complex　background.　The　proposed　method　utilized　the　width　of　each　kind　character,　the　interval　between　characters　and　the　relationship　within　the　left-right　structure　Chinese　character　to　improve　the　segmentation　accuracy.　Experimental　results　show　that　the　text　lines　from　the　data　plate　in　substation　transformer,　including　Chinese,　English,　Roman　numerals,　Arabic　numerals　and　symbols,　can　be　segmented　correctly.　Results　show　that　the　proposed　method　outperforms　two　existing　character　segmentation　methods　and　achieves　99.4%　precision　in　the　multi-language　data　plate　dataset.　©　2018　IEEE.