Text Detection and Extraction in Video Sequences

The objective of the paper is to In the video text detection stage, text locations in each frame are found via an unsupervised clustering performed on the connected components produced by the stroke width transform (SWT). Since SWT needs an accurate edge map, Efficient indexing and retrieval of digital video data is an important aspect of video database management. Video indexing involves content analysis of video sequences, which is usually a computationally intensive process. Since most video data are stored in compressed format, processing directly in the compressed domain offers the possibility of computationally more efficient algorithms. We present here an efficient algorithm for detecting and extracting text directly in MPEG video sequences. Most existing methods for text detection in video usually operate on raw pixel data and only output the location of the detected text regions in a single frame. Our algorithm makes use of the features encoded in compressed data to perform fast text detection. Motion information and the characteristics of text region continuity in multiple frames are then used to fine-tune the detected candidate text regions. Furthermore, characters are reliably extracted by an adaptive thresholding method after applying some noise reduction filtering in multiple frames. Such extracted characters can be directly fed into a conventional OCR system for recognition. Experimental results on several video sequences show that the proposed algorithm is able to detect and extract text in MPEG video sequences with various scene