Study of Embedded Font Context and Kernel Space Methods for Improved Videotext Recognition

Videotext refers to text superimposed on video frames. A videotext based Multimedia Description Scheme has recently been adopted into the MPEG-7 standard. A study of published work in the area of videotext extraction and recognition reveals that, despite recent interest, a reliable general purpose video character recognition (VCR) system is yet to be developed. In our research and development of a character recognition algorithm designed specifically for the low resolution output from automatic videotext extractors, we observed that raw VCR accuracies obtained using various classifiers including kernel space methods such as SVMs, are inadequate for accurate video annotation and browsing. Intelligent postprocessing mechanisms that are supported by general data characteristics of the domain are hence, required for performance improvement. We describe one such method, referred to as the Font Context Analysis, which works independently of the raw character recognition technique. As a result, it can be easily implemented in conjunction with other VCR algorithms being developed elsewhere, and offer the same performance gains. Experimental results on various video streams show notable improvements in recognition rates with our system incorporating a SVM-based character recognition mechanism and font context analysis.