This dissertation is focus on one of the key techniques related to MPEG-7 standards--the methods of video text detection, locating, and segmentation. By the affectionof the complexity of the background, the contrast of the text with the background, the size of the text, the orientation of the text, the shape of the text and motion method of the text, etc., there are much factors must be considered, so the algorithm is complex. The main purpose of this work is to decrease the complexity of this kind of method, increase its robusticity and accuracy. Several work has been done to achieve this goal:1. A text detection method of video frame based on edge and LH has been proposed. First, the edge image is acquired by Sobel operator, then it's LH is calculated after intensity and density detection, then the horizontal and vertical convex is detected based LH, finally the text is located. The experimental results demonstrate the simplicity and effectivity of the approach, which is free of the restriction of the text color, the text shape, and etc. This method works well in the horizontal and vertical text.2. A text detection method of video frame based on wavelet and LH has been proposed. This method, which based multiscale analysis, not only take horizontal and vertical high frequency component into consideration, but also considered the diagonal high frequency component. First, the edge image is acquired by Wavelet analysis which has fine temporal-frequency local feature and multiscale feature, then it's LH is calculated after intensity and density detection, then the horizontal and vertical convex is detected based LH, finally the text is located. By adopting multiscale method, it can detect variable font size. The experimental results demonstrate the simplicity and effectivity of the approach, which is free of the restriction of the text color, the text shape, and etc. This method also works well in the horizontal and vertical text.3. Two approaches based on morphology to detect video text are proposed. In the first approach, the high frequency component of the text is first acquired by wavelet