Video OCR for Video Indexing

Video OCR is a technique that can greatly help to locate the topics of interest in video via the automatic extraction and reading of captions and annotations. Text in video can provide key indexing information. Recognizing such text for search application is critical. Major difficult problem for character recognition for videos is degraded and deformated characters, low resolution characters or very complex background. To tackle the problem preprocessing on text image plays vital role. Most of the OCR engines are working on the binary image so to find a better binarization procedure for image to get a desired result is important.Accurate binarization process minimizes the error rate of video OCR.

[1]  M. Mori,et al.  Robust character recognition using adaptive feature extraction , 2008, 2008 23rd International Conference Image and Vision Computing New Zealand.

[2]  Richard M. Schwartz,et al.  Videotext OCR using hidden Markov models , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[3]  Tsung-Han Tsai,et al.  A Comprehensive Motion Videotext Detection Localization and Extraction Method , 2006, ICCCAS 2006.

[4]  Ellen K. Hughes,et al.  Video OCR for digital news archive , 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.

[5]  Takeo Kanade,et al.  Video OCR: indexing digital news libraries by recognition of superimposed captions , 1999, Multimedia Systems.

[6]  David S. Doermann,et al.  Automatic text detection and tracking in digital video , 2000, IEEE Trans. Image Process..

[7]  Tao Wang,et al.  Robust Text Stroke Extraction from Video , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[8]  Michael R. Lyu,et al.  A comprehensive method for multilingual video text detection, localization, and extraction , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Peng Tianqiang,et al.  A robust video text extraction method based on text traversing line and stroke connectivity , 2008, 2008 9th International Conference on Signal Processing.