Research On The SOM-based Method Of Video Artificial Text Detection

Artificial text in video is an important solution of expressing the semantic information of video. The effective detection, location and extraction of artificial text have very great significance for the understanding and retrieval of video and other applications.By the depth analysis of the existing text detection method in which most method has the text location and language constraints, a video unlimited artificial text area detection algorithm has been proposed in this thesis. Firstly, by analyzing, the features of SOM, SOM neural network classifier based on supervised learning is proposed, so that SOM neural network is able to marked the pixels classified. Then, by getting detail knowledge of edge feature of the video artificial text area, Sobel edge detection operator is the effective way to extract the image edge features. The features abstracted were used to be a part of feature vector. Because there's a clear edge between video artificial text area and background, while the Sobel operator not only has the direction but also the edge it gets in general is greater than 2 pixels. So it can be used to describe the image edge features better. For the problem of different occurrences of artificial text area, combined structure of the classifier proposed, a basic form of feature vectors is given. Then the supervised SOM classifier is used to classify them to get the initial target text area. Finally, the morphology-based approach is used to process the target text area to remove the noise points to eliminate the probability of misclassifying of the classifier. Then perform the post-processing of the precise positioning target text area. Experiments show that the method proposed can effectively detect and locate the video unlimited artificial text area.