US 9,542,752 B2Granted

Document image compression method and its application in document authentication

AssigneeKonica Minolta Laboratory U.S.A., Inc.

InventorsYibin Tian, Wei Ming

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Abstract

A method for compressing a bi-level document image containing text is disclosed. The document image is segmented into symbol images each representing a letter, numeral, etc. in the document. The symbol images are classified into a plurality of classes, each class being associated with a template image and a class index. Classification is done by comparing each symbol to be classified with template of existing classes, using a number of image features including zoning profiles, side profiles, topology statistics, and low-order image moments. These image features are compared using a tolerance based method to determine whether the symbol matches the template. After classification, certain classes that have few symbols classified into them may be merged with other classes. In addition, the template images of the classes are down-sampled, where the final sizes of the template images are dependent on the likelihood of confusion of the template with other templates.

Claims14

Dependent claims

A method for compressing a binary image representing a document containing text regions, the method comprising:

(a) segmenting the text regions into a plurality of symbol images, each symbol image representing a symbol of text, each symbol image being bound by a bounding box having a location and a size;
(b) classifying each symbol image obtained in step (a) into one of a plurality of classes, each class being represented by a template image and a class index, including, for each symbol image being classified:
(b1) comparing the symbol image with each template image to determine whether they match each other;
(b2) if a match is found in step (b1), recording the class index corresponding to the matched template in association with the symbol image being classified;
and
(b3) if no match is found in step (b1), adding a new class to the plurality of classes, by using the image of the symbol image being classified as the template image of the new class and assigning a class index to the new class, and recording the class index in association with the symbol image being classified;
(c) merging a first one of the plurality of classes into a second one of the plurality of classes if a total number of symbol images classified into the first class in step (b) is below a threshold and a measure of confidence of classification for the first class is below another threshold;
(d) resizing, after step (c), the template image of each class to a final size;
and
(e) storing, as compressed image data, the resized template image for each of the plurality of classes along with its class index, the bounding box location and size for each symbol image obtained in step (a), and the class index for each symbol image obtained in step (b2) or (b3).

7 more elements

A computer program product comprising a computer usable non-transitory medium having a computer readable program code embedded therein for controlling a data processing apparatus, the computer readable program code configured to cause the data processing apparatus to execute a process for compressing a binary image representing a document containing text regions, the process comprising:

(a) segmenting the text regions into a plurality of symbol images, each symbol image representing a symbol of text, each symbol image being bound by a bounding box having a location and a size;
(b) classifying each symbol image obtained in step (a) into one of a plurality of classes, each class being represented by a template image and a class index, including, for each symbol image being classified:
(b1) comparing the symbol image with each template image to determine whether they match each other;
(b2) if a match is found in step (b1), recording the class index corresponding to the matched template in association with the symbol image being classified;
and
(b3) if no match is found in step (b1), adding a new class to the plurality of classes, by using the image of the symbol image being classified as the template image of the new class and assigning a class index to the new class, and recording the class index in association with the symbol image being classified;
(c) merging a first one of the plurality of classes into a second one of the plurality of classes if a total number of symbol images classified into the first class in step (b) is below a threshold and a measure of confidence of classification for the first class is below another threshold;
(d) resizing the template image of each class to a final size;
and
(e) storing, as compressed image data, the resized template image for each of the plurality of classes along with its class index, the bounding box location and size for each symbol image obtained in step (a), and the class index for each symbol image obtained in step (b2) or (b3).

7 more elements

Dependencies are inferred from the claim text.

Facts

Application: 14/951,701Filed 25 Nov 2015 · continuation
Priority: 28 Dec 2012
Published: US 2016/0078632 A117 Mar 2016
Granted: 10 Jan 2017
Term ends: Not estimatedThe source records don’t support an estimate.

Classification23 codes

G06V 30/15
Segmentation of character regions: cutting or merging image elements, e.g. region growing, watershed or clustering-based techniques
G06F 18/28
Analysing: determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
G06T 7/11
Segmentation; Edge detection: region-based segmentation
G06V 20/80
Scenes; Scene-specific elements: recognising image objects characterised by unique random patterns

Family

Application13/730,757Parent of this continuation · filed 28 Dec 2012
US 2016/0078632 A1Pre-grant publication · published 17 Mar 2016
US 9,230,383 B2Parent of this continuation
US 2014/0185933 A1Published application
US 9,542,752 B2This patent

From the US filing history. Foreign family members aren’t listed.