US 10,592,738 B2Granted

Cognitive document image digitalization

AssigneeInternational Business Machines Corporation

InventorsKevin Northrup, Craig M. Trim, Badr Khamis, Karan Sehgal, Abisola Adeniran, Chandrashekhar Padole

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Abstract

Methods, computer program products, and systems are presented. The methods include, for instance: obtaining a document image with objects and identifying microblocks corresponding to each object. Analyzing a position of a microblock for collinearity with another microblock based on respective positional characteristics and adjustable collinearity parameters. Collinear microblocks are identified into a macroblock and computational data of a key-value pair is created from the macroblock. A heuristic confidence level is associated with the key-value pair. Also based on data cluster formation, a table may be classified and data extracted.

Claims13

Dependent claims

A computer implemented method for extracting data from a document image comprising:

obtaining, by one or more processor of a computer, the document image, wherein the document image includes a plurality of objects;
identifying a plurality of microblocks respectively corresponding to the plurality of objects, wherein each of the microblocks is associated with a content, a position, and a style;
discovering that a first microblock is collinear with a second microblock based on respective positional characteristics of the first microblock and the second microblock and adjustable collinearity parameters;
identifying a macroblock with the first microblock and the second microblock, wherein the macroblock constitutes a key-value pair that is computational by computer programs;
associating a confidence level with the key-value pair based on heuristics;
and
communicating the key-value pair and the associated confidence level to a user.

4 more elements

A computer program product comprising:

a computer readable storage medium readable by one or more processor and storing instructions for execution by the one or more processor for performing a method for extracting data from a document image comprising:
obtaining the document image, wherein the document image includes a plurality of objects;
identifying a plurality of microblocks respectively corresponding to the plurality of objects, wherein each of the microblocks is associated with a content, a position, and a style;
discovering that a first microblock is collinear with a second microblock based on respective positional characteristics of the first microblock and the second microblock and adjustable collinearity parameters;
identifying a macroblock with the first microblock and the second microblock, wherein the macroblock constitutes a key-value pair that is computational by computer programs;
associating a confidence level with the key-value pair based on heuristics;
and
communicating the key-value pair and the associated confidence level to a user.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/829,304Filed 1 Dec 2017
Published: US 2019/0171873 A16 Jun 2019
Granted: 17 Mar 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

G06V 30/413
Analysis of document content: classification of content, e.g. text, photographs or tables
G06F 18/217
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: validation; Performance…
G06V 30/10
Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition: character recognition
G06V 30/414
Analysis of document content: extracting the geometrical structure, e.g. layout tree; Block segmentation, e.g. bounding boxes for graphics or text