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.

Text that 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 classification. In this work, we present algorithms for detecting and tracking text in digital video. Our system implements a scale-space feature extractor that feeds an artificial neural processor to detect text blocks. Our text tracking scheme consists of two modules: a sum of squared difference (SSD)-based module to find the initial position and a contour-based module to refine the position. Experiments conducted with a variety of video sources show that our scheme can detect and track text robustly.

In this paper we present a new approach toward processing of multimedia documents. We use intelligence derived from media and processing dependencies and distribute the processing according to logical progression of the media processing tasks. To achieve this we devised a management strategy which takes as an input a user defined problem with pre-coded processing tools and maps to distributable tasks which have an associated resource requirements. At the last step of the processing, the available processing nodes, which vary in available resources, are matched to the processes. An example demonstrating the capabilities of this system is presented.

Multimedia content is designed for many purposes and the services that are associated with them rely heavily on technologies that address representation, processing, and transmission of that content. This paper presents a new model that refines the multimedia documents for mobile environments while integrating information technology components to address strict requirements. We present new ways to structure and represent the multimedia content to fit into the traditional document framework so that we can serve the document for an alternative presentation ordering, cross-media translation, and scalable renditions. We propose techniques that utilize the structure of documents across several levels in a manner that applies semantic description to provide scalable multimedia services. A modern multimedia document consumption scenario is introduced where a mobile collaborative working environment is demonstrated utilizing the proposed techniques.

We propose and implement a method for detecting duplicate documents in very large image databases. The method is based on a robust "signature" extracted from each document image which is used to index into a table of previously processed documents. The approach has a number of advantages over OCR or other recognition based methods, including speed and robustness to imaging distortions. To justify the approach and test the scalability, we have developed a simulator which allows us to change parameters of the system and examine performance for millions of document signatures. A complete system is implemented and tested on a test collection of technical articles and memos.

Symbolic compression of document images provides access to symbols found in document images and exploits the redundancy found within them. Document images are highly structured and contain large numbers of repetitive symbols. We have shown that while symbolically compressing a document image we are able to perform compressed-domain processing. Symbolic compression forms representative prototypes for symbols and encode the image by the location of these prototypes and a residual (the difference between symbol and prototype). We analyze the rate-distortion tradeoff by varying the amount of residual used in compression for both distance- and row-order coding. A measure of distortion is based on the performance of an OCR system on the resulting image. The University of Washington document database images, ground truth, and OCR evaluation software are used for experiments.

Document images belong to a unique class of images where the information content is contained in the language represented by a series of symbols on the page, rather than in the visual objects themselves. For this reason, it is essential to preserve the fidelity of individual components when considering methods of compression. Likewise the component level structure should be a prime consideration when ordering information for lossy or progressive transmission. We refine our work on document image compression as it applies to transmission and retrieval. We first overview the basic compression scheme, then describe a structural hierarchy which provides desirable properties for transmission. We present the results of a rate distortion experiment and discuss the implications for network applications.

This paper describes a new approach to document management in a multimedia domain. A 'hyperdocument' is defined to denote a cross-media access to information formalized by a document framework. A hyperdocument management system extends the constituent components of a document to include multi-media objects and addresses issues such as content-based compression, optimized transmission and compressed domain processing. The semantics of embedded structures are organized via a document management interface to perform document related tasks while achieving an enhancement in network traffic, as well as better response rates in media-demands for multimedia applications. The representation includes flexible application interface and the implementation includes efficient integration in a compact form. We describe each of these features, issues involved, and present experiments performed with different integrable domains.

Maintaining a document in image form is often preferable in order to avoid the high cost of manual conversion or the introduction of large numbers of errors by automatic OCR and/or graphics interpretation. The large volume of data in the image can be greatly reduced by using compression techniques. Text-intensive document images typically have a great deal of redundancy in the bitmap representations of symbols, and we make use of that redundancy for compression by clustering components, representing each cluster by a template and encoding the error. Our method is novel in modeling the error associated with each cluster and in preserving structure, an important component for readability and processing.

In this paper we describe a structural compression technique to be used for document text image storage and retrieval. The primary objective is to provide an efficient representation, storage, transmission and display. A secondary objective is to provide an encoding which allows access to specified regions within the image and facilitates traditional document processing operations without requiring complete decoding. We describe an algorithm which symbolically decomposes a document image and structurally orders the error bitmap based on a probabilistic model. The resultant symbol and error representations lend themselves to reasonably high compression ratios and are structured so as to allow operations directly on the compressed image. The compression scheme is implemented and compared to traditional compression methods.