  The amount of digital video data is increasing over the world. It highlights
the need for efficient algorithms that can index, retrieve and browse this data
by content. This can be achieved by identifying semantic description captured
automatically from video structure. Among these descriptions, text within video
is considered as rich features that enable a good way for video indexing and
browsing. Unlike most video text detection and extraction methods that treat
video sequences as collections of still images, we propose in this paper
spatiotemporal. video-text localization and identification approach which
proceeds in two main steps: text region localization and text region
classification. In the first step we detect the significant appearance of the
new objects in a frame by a split and merge processes applied on binarized edge
frame pair differences. Detected objects are, a priori, considered as text.
They are then filtered according to both local contrast variation and texture
criteria in order to get the effective ones. The resulted text regions are
classified based on a visual grammar descriptor containing a set of semantic
text class regions characterized by visual features. A visual table of content
is then generated based on extracted text regions occurring within video
sequence enriched by a semantic identification. The experimentation performed
on a variety of video sequences shows the efficiency of our approach.
