Hewlett-packard Development Company, L.P.

Purdue Research Foundation

Peter Bauer

Jan Philip Allebach

Charles Addison Bouman

Todd J Harris

Yandong Guo

Zhenhua Hu

Dejan Depalov

Perry Victor Lea

Mark Edward Shaw

Mark Q Shaw

Yifeng Wu

Roger Scott Twede

Eli S Saber

Kadagattur Gopinatha Srinidhi

Lixia Li

Yafei Mao

David C Day

Osborn De Lima

Litao Hu

Yufang Sun

Shruty Janakiraman

Jay Allebach

Vandana Roy

Yandong Guo Guo

In some examples, a computing device can include a processor resource and a non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause the processor resource to: identify a plurality of regions within a captured document, determine a quantity of vertical transitions and horizontal transitions within the plurality of regions, and select an output resolution for the plurality of regions based on the quantity of vertical transitions and horizontal transitions within the plurality of regions.

Output resolution selections

In some examples, an imaging device can include a processing resource and a memory resource storing instructions to cause the processing resource to perform a feature extraction process to extract a gamut-based feature included in a plurality of pixels of a scanned image to determine whether the scanned image includes a particular marking, apply a classification model to the scanned image, and optimize the scanned image based on the classification of the scanned image.

Feature extractions to optimize scanned images

Examples disclosed herein relate to identifying a plurality of content areas of a document to be scanned, classifying each of the plurality of content areas into a content type, determining a minimum scanning resolution to maintain readability for each of the plurality of content areas according to the classified content type, and performing a scan of the document to a digital file, wherein each of the plurality of content areas is scanned at least at the determined minimum scanning resolution to maintain readability of the respective content area.

Determining minimum scanning resolution

Example implementations relate to detecting document objects. For example, detecting document objects may include a system comprising a pre-processing engine to establish a threshold for a document, wherein a structure of the document is unknown, a detection engine to detect a candidate area in the document, using a Hough transform and connected component analysis to merge detected candidate areas in the Hough transform to a same document object in the document, and a classification module to classify the candidate area as a document object or not a document object.

Detecting document objects

A method of creating a hierarchical dictionary comprises, with a processor, extracting a number of symbols from a first image, constructing a number of refinement dictionary entries based on the symbols, the refinement dictionary entries forming a refinement dictionary, grouping a number of the refinement dictionary entries into clusters to form a number of refinement dictionary entry clusters, and constructing a number of direct dictionary entries for each of the refinement dictionary entry clusters, the direct dictionary entries forming a direct dictionary.

Creation of hierarchical dictionary

A system and method for text line detection are described Examples include detection of symbols in an image received from an image-capturing device. In examples, for each of at least some of the symbols, neighboring symbols within a local region a given distance from the symbol are analyzed in order to determine a direction for a line in the local region. In examples, based on the determined directions for the lines, text lines in the image are identified.

Tex line detection

The present disclosure includes a system and method for symbol compression using conditional entropy estimation. One method for symbol compression using conditional entropy estimation includes approximating a quantity of symbol encoding bits for a number of symbols using a conditional entropy estimation. Dictionary entries are generated from the number of symbols so as to minimize a total bit-stream quantity. The total bit-stream quantity includes at least the approximated quantity of symbol encoding bits and a quantity of dictionary entries encoding bits. The symbols are encoded using the dictionary entries as a reference.

Symbol compression using conditional entropy estimation

In one embodiment, a method of detecting document orientation includes capturing a document image, binarizing each subimage of the document image to retain textual content and eliminate graphic and noise content from the document image, detecting portrait or landscape orientation based on values computed from strip-based projection profiles, and detecting up or down text orientation based on a text-asymmetry ratio computed from strip-based projection profiles.

System and method for document orientation detection

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Boise, ID, United States of America

Peter Bauer