Google Inc.

Guillaume Raphael Poncin

Nemanja Spasojevic

Dan S Bloomberg

Siraj Khaliq

Francois-Marie Lefevere

Viresh Ratnakar

A signature for a page of text is generated. The signature serves as an identifier of the text page. Positions of words in a text page are determined. Positions of multiple second words in the text page are determined relative to the position of a first word in the text page. A signature value is generated that describes the second word positions relative to the first word position. The signature value is stored. Additional signatures for the text page can be generated, each signature describing positions of other words in the text page relative to a word in the text page for which the signature is being generated. The signatures can be used to compare the text page to another text page and generate a measure of similarity that describes the result of the comparison.

Comparing text pages using image features based on word positions

Relationships between digital text volumes in a corpus are identified by comparing a plurality of digital text volumes in the corpus to identify clusters of similar volumes. For a given cluster, the pages of pairs of volumes within the cluster are compared to determine page similarity data, the page similarity data indicating degrees of similarity between the compared pages. The relationships between pairs of volumes in the clusters are classified based on the similarity data.

Identifying similar digital text volumes

Semantically meaningful segments of an image of a document, such as tables of contents, page numbers, footnotes, and the like, are identified. These segments form a model of the document image, which may then be rendered differently for different client devices. The rendering may be based on a display parameter provided by a client device, such as a display resolution of the client device, or a requested display format.

Display of document image optimized for reading

Techniques for scanning a document, such as a book, magazine, or catalog, are described in which artifacts (e.g., a hand or other body part) in the scanned image are automatically detected. When the artifact is determined to be in a critical portion of the image, a signal may be issued to re-scan the image.

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Stanford, CA, United States of America

Guillaume Raphael Poncin