In this paper, we describe a method for storing an entire video, animation sequence, or any other media type, on paper. The method is based on printing a key frame from a video on paper along with a barcode that encodes the motion information and other auxiliary information in MPEG-4 format. Unlike other video barcode systems in the prior art, a barcode in our system does not contain a link to the video clip; instead it contains motion information. A client device applies the motion information to an image of the video key frame to obtain full motion video. Therefore, the paper document is a self contained representation of a video clip and access to a server is not required. We modified an MPEG-4 [1] encoder and decoder to implement the video flip-book encoder and decoder. Experiments show that it is possible to encode several seconds of video on paper using our method. This is sufficient to create small animations for some printed materials such as video greeting cards and children's books.

A system is presented that uses texture to retrieve and browse images stored in a large document image database. A method of graphically generating a candidate search image is used that shows the visual layout and content of a target document. All images similar to this candidate are returned for the purpose of browsing or further query. The system is accessed using a World Wide Web (Web) browser. Applications include the retrieval and browsing of document images including newspapers, fares and business letters.

Image continuation, the problem of restoring images that have undefined pixel values at known pixel locations. In document images, undefined pixel values at known locations can have several causes, such as the occurrence of a scratch or blemish on the contact glass of a scanner or the malfunction of a CCD sensor. Once identified, these known locations provide critical information that can be exploited to continue the image from the reliable parts to these unreliable regions. The authors present a family of algorithms accomplishing image continuation, using a small, simple kernel function adaptively applied on successive iterations. Some theoretical properties of this family other than convergence have been established and several examples are presented to illustrate the behavior of the algorithms.>

Abstract—The results of three experiments with Highleyman's hand-printed characters are reported. Nearest-neighbor classification is used to explain the high error rates (42 to 60 percent) obtained by general statistical procedures. An error rate of 32 percent is obtained by preceding piecewise-linear classification by edge-detecting preprocessing. The minimum human error rate is estimated, and suggested as a performance standard.

The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability of errorRof such a rule must be at least as great as the Bayes probability of errorR^{\ast}--the minimum probability of error over all decision rules taking underlying probability structure into account. However, in a large sample analysis, we will show in theM-category case thatR^{\ast} \leq R \leq R^{\ast}(2 --MR^{\ast}/(M-1)), where these bounds are the tightest possible, for all suitably smooth underlying distributions. Thus for any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.