Sliding text recognition in broadcast news

In this study, a method is proposed for the recognition of sliding text in broadcast news videos. Video frames are converted into binary images, from which horizontal and vertical projection histograms are extracted to determine the position of the text band. After some noise removal operations, which make use of the redundancy between video frames, the text image is segmented into individual characters by connected component analysis. Template matching is used for character recognition. The strings obtained by recognition in consecutive images are aligned in space and time, which leads to a complete transcription of the whole program. Some similarly shaped characters may be confused with each other. To overcome this, transformation based learning algorithm is used and corrective rules are learned from a training text. The proposed method achieves 99% character recognition accuracy and 92% word recognition accuracy.