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We present a useful method for assessing the quality of a typewritten document image and automatically selecting an optimal restoration method based on that assessment. We use five quality measures that assess the severity of background speckle, touching characters, and broken characters. A linear classifier uses these measures to select a restoration(More)
We describe a system that automatically identifies the script used in documents stored electronically in image form. The system can learn to distinguish any number of scripts. It develops a set of representative symbols (templates) for each script by clustering textual symbols from a set of training documents and representing each cluster by its centroid.(More)
We present a new framework for rapid development of mixed-initiative dialog systems. Using this framework, a developer can author sophisticated dialog systems for multiple channels of interaction by specifying an interaction modality, a rich task hierarchy and task parameters, and domain-specific modules. The framework includes a dialog history that tracks(More)
This paper explores the use of script identification vectors in the analysis of multilingual document images. A script identification vector is calculated for each connected component in a document. The vector expresses the closest distance between the component and templates developed for each of thirteen scripts, including Arabic, Chinese, Cyrillic, and(More)