DL '97 Proceedings of the second ACM international conference on Digital libraries
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video OCR for Digital News Archive
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Automatic text detection and tracking in digital video
IEEE Transactions on Image Processing
Binarization of color document images via luminance and saturation color features
IEEE Transactions on Image Processing
Localizing and segmenting text in images and videos
IEEE Transactions on Circuits and Systems for Video Technology
A spatial-temporal approach for video caption detection and recognition
IEEE Transactions on Neural Networks
A hybrid text segmentation approach
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Text segmentation in complex background based on color and scale information of character strokes
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
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In this paper we propose a robust text segmentation method in complex background. The proposed method first utilizes the K-means algorithm to decompose a detected text block into different binary image layers. Then an effective post-processing is followed to eliminate background residues in each layer. In this step we develop a group of robust constraints to characterize general text regions based on color, edge and stroke thickness. We also propose the components relation constraint (CRC) designed specifically for Chinese characters. Finally the text image layer is identified based on the periodical and symmetrical layout of text lines. The experimental results show that our method can effectively eliminate a wide range of background residues, and has a better performance than the K-means method, as well as a high speed.