A Text Region Location Method Based on Connected Component
YAO Jin-Liang1, WENG Lu-Bin2 , WANG Xiao-Hua1
1.School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310018 2.Integrated Information System Research Center,Institute of Automation,Chinese Academy of Sciences,Beijing 100190
Abstract：Text region location is important to text recognition and retrieval in images of complex background. The existing methods with precision and recall rate have high computational complexity. These methods are unpractical real environment. A text region location method is proposed based on component filtering and K-means clustering. Firstly, the input image is segmented into three layers by an adaptive image segmentation method, and the components are extracted from the character layers. Then, the features of the component are obtained, and Adaboost classifier is used to filter non-character components. The candidates of character components are grouped into text regions by K-means clustering based on the position and layer of the component. The experimental results demonstrate that the precision and the recall rate of the proposed approach is almost the same that of as the other methods, and the proposed method has lower computational complexity.
姚金良，翁璐斌，王小华. 一种基于连通分量的文本区域定位方法[J]. 模式识别与人工智能, 2012, 25(2): 325-331.
YAO Jin-Liang, WENG Lu-Bin, WANG Xiao-Hua. A Text Region Location Method Based on Connected Component. , 2012, 25(2): 325-331.
[1] Kim I K,Keechul J,Jin H.Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm.IEEE Trans on Pattern Analysis and Machine Intelligence,2003,25(12):1631-1639 [2] Chen Datong,Bourlard H,Thiran J P.Text Identification in Complex Background Using SVM // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Kauai,USA,2001,II: 621-626 [3] Hanif S M,Prevost L.Text Detection and Localization in Complex Scene Images Using Constrained AdaBoost Algorithm // Proc of the International Conference on Document Analysis and Recognition.Catalonia,Spain,2009,I: 1-5 [4] Wanga K,Kangasb J A.Character Location in Scene Images from Digital Camera.Pattern Recognition,2003,36(10): 87-99 [5] Zhong Y,Karu K,Jain A K.Locating Text in Complex Color Images.Pattern Recognition,1995,28(10): 1523-1536 [6] Lucas Simon M.ICDAR 2005 Text Locating Competition Results // Proc of the International Conference on Document Analysis and Recognition.Seoul,Korea,2005,I: 80-84 [7] Zhu Kaihua,Qi Feihu,Jiang Renjie,et al.Using Adaboost to Detect and Segment Characters from Natural Scenes // Proc of the 1st International Workshop on Camera-Based Document Analysis and Recognition.Seoul,Korea,2005: 52-59 [8] Richard D O,Hart P E,Stork D G.Pattern Classification.2nd Edition.New York,USA: Wiley,2000
2012, Vol. 25 
