With the rapid advances in digital technology, more and more databases are multimedia in nature, containing images and video in addition to the textual information. Understanding the content of the images and video automatically by computer attracts more and more attention from international and national researchers. Text is an attractive feature for video annotation and indexing because it provides rich semantic information about the video. Therefore, it is an urgent and challenging task to develop a frame-work which can detect, extract and recognize texts from complex backgrounds effectively. Aiming at this goal, the following research work has been conducted. 1、 This thesis presents a text detection method based on edge intensity and pyramid strategies. Firstly, edge-map is acquired from the original image, which is used to filter out non-text region based on edge intensity feature. Then, connected-components analysis, vertical projection and horizontal projection are applied to get the candidate text lines. In order to detect different size of text, pyramid decomposition is utilized. Finally, candidate text lines of multi-scales are fused together to get the final result. Experiment results show that this method is efficient and not influenced by size of text and illumination etc. 2、 This thesis presents another text detection method via sparse representation. This method utilizes coarse to fine text detection framework. In coarse detection stage, quick edge intensity filter and connected-components analysis are utilized to get the candidate text lines. In fine detection stage, the candidate text lines are verified with non-complete dictionary generated by sparse presentation classification. The preliminary experiments show that this method has better result compared to traditional methods. 3、 It's difficult to extract text from video image with conventional binarization method because of its complex background. Thus we utilized connected-components analysis and histogram to convert the video image to picture of white text with black background or black text with white background which can easily be recognized by OCR software. The preliminary experiments show that this method has good performance when there is obvious contrast between the color of text and the color of background.
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