Nowadays digital cameras, mobile phone cameras and other digital products are becoming more and more popular, people can obtain the images and videos easily, so the number of image and videos grows rapidly. With the rapid development of individual PC and the Internet makes the image and video have become important carriers of information exchange in our daily life. How to make the computer automatically understand and use the contents of images and videos has become a hotspot of current research in the field of image processing and computer vision. Compared to the color, texture, shape and other low-level image features, embedded text in images and videos is usually associated with image content directly. So if the text in images or video frames can be detected, extracted and recognized, it can provide important clues for understanding the image and video content. The traditional character recognition techniques can effectively process high quality scan documents, but encounter many difficulties for recognizing text in the image with complex background. Therefore, we need to provide effective solutions from theory and technology. The thesis concentrates on the study of text detection and extraction in images or video frames with complex background, the main contents include: First, this thesis proposes a text detection method based on adaptive corner detection and fusion. The method mainly uses image corners to locate and detect text regions. First, image complexity is defined by gray change and edge distribution, then according to the image complexity, candidate text regions can be achieved by corner detection and fusion. Further edge projection analysis is adopted to locate the text regions accurately, finally the SVM classifier is used for text classification which remove the false alarms. The experiments on video data sets show that the proposed method has a high recall rate and speed. Secondly, this thesis proposes a text detection method based on random forests. The method mainly contain coarse detection and text verification. In coarse detection we first use MVD color edge detection and BST binarization to get the binarized edge map, and then edge density distribution is used to remove simple background. At last connected component analysis based on geometry and color characteristics of text region is used to grow candidate text regions. After the coarse detection, text refinement and verification are used to locate the text line accurately and ...
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