Research On Text Detection In Images And Frames Using Multi-Scale And Homogeneity

Text embedded in picture and video is an important resource for picture's high level content understanding, it contains some useful information, for instance street names, shop names, signposts, traffic signals, titles, etc. That information is important in indexing, compressing of the picture and video.In general, it can be divided into artificial text and scene text according to the existent form of text, present research mainly concentrates on artificial text, and the research of the scene text has just started. The mainly method is using different image processing methods to enhance character's corner, color and texture features, then classifying text and background to form the candidate regions, then rectifying the real text area through the postprocess.In this thesis, we study on text detection. It includes five parts: pre-process, multi-scale analysis, feature extraction, classification and text area extraction. The size of text in scene image varies a lot, to extract different size text, in multi-scale process, we make the image into three resolutions: 1/1, 1/2, 1/4. In feature extraction step, we apply homogeneity into text detection, and we compare using the gradient, edge extract and homogeneity mapping to enhance corners and texture features, and then use a slip window to get different kinds of texture features as the feature vector, and then after comparing the accuracy result of SVM and BP neural network, we choose SVM as the classifier. We get the candidate area by integrating the SVM results of different scales, and use density-based candidate area confirmation and image smoothing postprocess to get the final text area.Through experiment, the statistics in Homogeneity area play well in feature extract and SVM can be a good classifier, so using this algorithm can reach an ideal result.