Abstract :
In this paper, we presents a new binarization approach to extract text pixels from complex background in video frames. The binarization computation is a crucial step for, video text recognition, which can greatly increase the recognition, accuracy of an OCR software. The proposed approach consists, of four phases. First, the text polarity is determined, i.e. light text with dark background or dark text with light background., Then the pixels in the given image are clustered into K clusters, using the K-means algorithm in the RGB color space and the, text cluster is selected based on the text polarity. Further, the, MRF Model is exploited to get the binarization result. Finally, the, result is further refined by the Log-Gabor filter. The Experimental, results on a large dataset show that the significant gains have been, obtained according to the segmentation performance on the pixel, level as well as the OCR accuracy.
Keywords :
Gabor filters; feature extraction; image colour analysis; image segmentation; learning (artificial intelligence); optical character recognition; video signal processing; K-means algorithm; MRF model; OCR software; RGB color space; binarization computation; dark background; dark text; light background; light text; log-Gabor filter; optical character recognition; overlay text binarization; red-green-blue color space; segmentation performance; text pixels extraction; text polarity; video frames; video text recognition; Accuracy; Clustering algorithms; Colored noise; Image color analysis; Optical character recognition software; Text recognition; K-means; MRF; binarization; video text;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on