Recognition and Transition Frame Detection of Arabic News Captions for Video Retrieval

The authors have conducted studies on recognizing Arabic news captions to develop a system for video retrieval to index and edit Arabic broadcast programs daily received and stored in big database. This paper describes a dedicated OCR for recognizing low resolution news captions in video images. News caption recognition system consisting of text line extraction, word segmentation and segmentation-recognition of words is developed and the performance was experimentally evaluated using datasets of frame images extracted from AIJazeera broadcasting programs. Character recognition of moving news caption is difficult due to combing noise yielded by the interlacing of scan lines. A technique to detect and eliminate the combing noise to correctly recognize the moving news caption is proposed. This paper also proposes a technique based on inter-frame text difference to detect transition frame of still news captions. The technique to detect transition frames is necessary for efficient video retrieve and play. The proposed technique is experimentally tested and shown to be robust to quick motion of the background and is able to detect the transition frame correctly with the F-measure higher than 90%. When compared with the ABBY FineReader 11 (R) commercial OCR the dedicated OCR improves the recall of the Arabic characters in AIJazeera broadcasting news from 70.74% to 95.85% for non-interlaced moving news captions and from 23.82% to 96.29% for interlaced moving news captions.