Video text image segmentation method based on edge and color features

Before video text images are input to OCR software, text should be separated from background, because video texts often are embedded in complex background. There are two problems: one is that the color of background pixels may be similar with the color of text pixels, the other one is that the color of text pixels has variance caused by video compression and decompression. To solve the two problems, a new text image segmentation algorithm was introduced based on text edge and color features. First, the sample pixels set was got according to high frequency edge information of text. Second, K-means clustering method was applied to get segmentation seed pixels and radius, then segment text image into several text candidate images. Last, false text candidate images were excluded according to connected component property of text strokes. Experimental result shows that this method can separate text from background easily, and gets good OCR result.