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Text which either appears in a scene or is graphically added to video can provide an important supplemental source of index information as well as clues for decoding the video's structure and for classi cation. In this paper we present algorithms for detecting and tracking text components that appear within digital video frames. Our system implements a scale-space feature extractor that feeds an arti cial neural processor to extract textual regions and track their movement over time. The extracted regions can then be used as input to an appropriate Optical Character Recognition system which produces indexible keywords.

Symbolic document image compression relies on the detection of similar patterns in a document image and construction of a prototype library. Compression is achieved by referencing multiple pattern instances ("components") through a single representative prototype. To provide a lossless compression, however, the residual difference between each component and its assigned prototype must be coded. Since the size of the residual can significantly affect the compression ratio, efficient coding is essential. In this paper, we describe a set of residual coding models for use with symbolic document image compression that exhibit desirable characteristics for compression and rate-distortion and facilitate compressed-domain processing. The first model orders the residual pixels by their distance to the prototype edge. Grouping pixels based on this distance value allows for a more compact coding and lower entropy. This distance model is then extended to a model that defines the structure of the residue and uses it as a basis for continuous and packet reconstruction which provides desired functionality for use in lossy compression and progressive transmission.