Xerox Corporation

Ercan E Kuruoglu

Christopher R Dance

Alex S Taylor

Stuart Taylor

Mauritius Seeger

A summary of a captured document image is produced on the basis of detected handwritten annotations made to a document prior to image capture. The scanned (or otherwise captured) image is processed to detect annotations made to the document prior to scanning. The detected annotations can be used to identify features, or text, for use to summarize that document. Additionally, or alternatively, the detected annotations in one document can be used to identify features, or text, for use to summarize a different document. The summary may be displayed in expandable detail levels.

Using annotations for summarizing a document image and itemizing the summary based on similar annotations

A system enables users at different locations (e.g. different geographic locations) to work collaboratively on a document, and to view each other's annotations to the document. Each user can work with a paper document, and make handwritten annotations to the paper document. The system includes a plurality of workstations communicating with a server. Each workstation comprises a document-imaging device, such as a camera, for generating a digital image of the document. Handwritten annotations are identified by image processing, and new annotations made by each user can be tracked in real time. Image bitmaps of the annotations are distributed by the server to each workstation so that each user can view other user's annotations. In one view, the annotations from different users are superimposed in a combined image.

Method and apparatus for collaborative annotation of a document

An observed signal that is corrupted with impulse noise is recorded in a signal processing system of an image processing system or a digital subscriber line (xDSL). The observed signal that is recorded by the signal processing system includes a noise component and data component. The signal processing system estimates the parameters of an alpha-stable distribution using a modified iteratively reweighted least squares (IRLS) technique. The estimated parameters define a probability density function that is used to model the noise component of the observed signal. Once the parameters of the alpha-stable distribution are estimated, the signal processing system uses them to estimate model coefficients of a non-linear prediction filter such as a Volterra filter. Using the model coefficients, the non-linear prediction filter estimates the data component of the observed signal.

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Pisa, Italy

Ercan E Kuruoglu