Amazon Technologies, Inc.

Brent Eric Wood

Bradley Jeffery Behm

Robert Lovejoy Goodwin

Kenneth Paul Kiraly

John Lattyak

Gregg Elliott Zehr

Richard L Moore

Thomas A Ryan

Beryl Tomay

Brandon John Smith

Michael V Rykov

James C Slezak

Kevin R Cheung

Aviram Zagorie

Paul Edward Landry

Ajay M Desai

Jon Saxton

Karim A B Yafi

Nikunj Agarwal

Sheshagiri Rao

Girish Bajaj

Thomas M Fruchterman

Neil C Roseman

Xiaotian Guo

Described herein is a computer-implemented method for techniques relating to anomalous content marking and determination. A content marking request of anomalous content can be received by a computer system. A content marking count associated with the content can be determined for the content. A content marking ratio can be determined based on the content marking count. A parameter indicative of the anomalous status of the content can be determined based on the content marking count and/or content marking ratio, and the parameter can be compared to a threshold parameter. Alerts of anomalous content can be delivered at the user device based on the content marking count, the content marking ratio, the parameter or the comparison result between the parameter and the threshold parameter.

Anomalous content marking and determination

Systems and methods are disclosed for automatically classifying images of pages of a source, such as a book, into classifications such as front cover, copyright page, table of contents, text, index, etc. In one embodiment, three phases are provided in the classification process. During a first phase of the classification process, a first classifier may be used to determine a preliminary classification of a page image based on single-page criteria. During a second phase of the classification process, a second classifier may be used to determine a final classification for the page image based on multiple-page and/or global criteria. During an optional third phase of classification, a verifier may be used to verify the final classification of the page image based on verification criteria. If automatic classification fails, the page image may be passed on to a human operator for manual classification.

Automatically classifying page images

A system and method are disclosed for automatically classifying images of pages of a source, such as a book, into classifications such as front cover, copyright page, table of contents, text, index, etc. In one embodiment, three phases are provided in the classification process. During a first phase of the classification process, a first classifier may be used to determine a preliminary classification of a page image based on single-page criteria. During a second phase of the classification process, a second classifier may be used to determine a final classification for the page image based on multiple-page and/or global criteria. During an optional third phase of classification, a verifier may be used to verify the final classification of the page image based on verification criteria. If automatic classification fails, the page image may be passed on to a human operator for manual classification.

Method and system for automatically classifying page images

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Seattle, WA, United States of America

Brent Eric Wood