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Efficient content based retrieval of image and video databases is an important emerging application due to rapid proliferation of image and digital video data on the internet and corporate intranets and exponential growth of video content in general. Text either embedded or superimposed within video frames is very useful for describing the semantic content of the frames, as it enables both keyword and free-text based search, automatic video logging, and video cataloging. Extracting text directly from video data becomes especially important when closed captioning or speech recognition is not available to generate textual transcripts of audio or when video footage that completely lacks audio needs to be automatically annotated and searched based on frame content.
Towards building a video query system, developed a scheme for automatically extracting text from digital image and videos for content annotation and robust text extract which can handle complex backgrounds in video frames, deal with different font sizes, font styles, and font appearances such as normal and inverse videos. The algorithm results in segmented characters from video frames that can be directly processed by an OCR system to produce ASCII text. Results from the experiments obtained from MPEG video streams demonstrate the good performance if our systems in terms of text identification accuracy and computational efficiency.
