Document image blur assessment

Patent metadata

Field	Value
Publication number	US-12573221-B2
Application number	US-202318193736-A
Country	US
Kind code	B2
Filing date	Mar 31, 2023
Priority date	Dec 30, 2022
Publication date	Mar 10, 2026
Grant date	Mar 10, 2026

Abstract

The disclosure includes a system and method for determining a first measure of blur value associated with a first portion of a document under test; determining a second measure of blur value associated with a second portion of the document under test; determining whether an inconsistency in a set measure of blur values associated with the document under test is present, wherein the set of measure of blur values associated with the document under test includes the first measure of blur value and the second measure of blur value; and modifying a likelihood that the document is accepted or rejected based on whether the inconsistency is absent or present, respectively.

First claim

What is claimed is:

1 . A method comprising:
determining, using one or more processors, a first measure of blur value associated with a first portion of an image of a document under test based on pixel information representing the first portion of the document under test in the image; determining, using the one or more processors, a second measure of blur value associated with a second portion of the image of the document under test based on pixel information representing the second portion of the document under test in the image; determining, using the one or more processors, whether an inconsistency in a set of measure of blur values associated with the document under test is present, wherein the set of measure of blur values associated with the document under test includes the first measure of blur value and the second measure of blur value; and modifying, using the one or more processors, a likelihood that the document is accepted or rejected based on whether the inconsistency is absent or present, respectively.

2 . The method of claim 1 , wherein the first portion of the document under test is associated with a first bounding box generated using optical character recognition, and the second portion of the document under test is associated with a first bounding box generated using optical character recognition.

3 . The method of claim 1 , wherein an inconsistency exists when a difference between the first measure of blur and the second measure of blur satisfies a threshold.

4 . The method of claim 1 , wherein the first portion of the document under test is a first character in a first text string and the second portion of the document under test is a second character in the first text string.

5 . The method of claim 4 , the method further comprising:
determining a third measure of blur associated with the first text string at a field level; determining a fourth measure of blur associated with a second text string at the field level; comparing the third measure of blur and the fourth measure of blur; and determining based on the comparison whether a difference in blur at the field level exists.

6 . The method of claim 1 , wherein the first portion of the document under test is associated with a first text string and the second portion of the document under test is associated with a second text string.

7 . The method of claim 1 , wherein the first portion of the document under test is associated with a field label and the second portion of the document under test is a text field associated with the field label.

8 . The method of claim 1 , wherein the first measure of blur is determined by applying Canny edge detection to the first portion of the document under test and the second measure of blur is determined by applying Canny edge detection to the second portion of the document under test.

9 . The method of claim 1 , wherein the first measure of blur is determined by applying Laplacian variance detection to the first portion of the document under test and the second measure of blur is determined by applying Laplacian variance to the second portion of the document under test.

10 . The method of claim 1 , wherein the first measure of blur is determined by applying Cepstral techniques to the first portion of the document under test and the second measure of blur is determined by applying Cepstral techniques to the second portion of the document under test.

11 . A system comprising:
a processor; and a memory, the memory storing instructions that, when executed by the processor, cause the system to:
determine a first measure of blur value associated with a first portion of an image of a document under test based on pixel information representing the first portion of the document under test in the image;
determine a second measure of blur value associated with a second portion of the image of the document under test based on pixel information representing the second portion of the document under test in the image;
determine whether an inconsistency in a set of measure of blur values associated with the document under test is present, wherein the set of measure of blur values associated with the document under test includes the first measure of blur value and the second measure of blur value; and
modify a likelihood that the document is accepted or rejected based on whether the inconsistency is absent or present, respectively.

12 . The system of claim 11 , wherein the first portion of the document under test is associated with a first bounding box generated using optical character recognition, and the second portion of the document under test is associated with a first bounding box generated using optical character recognition.

13 . The system of claim 11 , wherein an inconsistency exists when a difference between the first measure of blur and the second measure of blur satisfies a threshold.

14 . The system of claim 11 , wherein the first portion of the document under test is a first character in a first text string and the second portion of the document under test is a second character in the first text string.

15 . The system of claim 14 , wherein the instructions, when executed, cause the system to:
determine a third measure of blur associated with the first text string at a field level; determine a fourth measure of blur associated with a second text string at the field level; compare the third measure of blur and the fourth measure of blur; and determine based on the comparison whether a difference in blur at the field level exists.

16 . The system of claim 11 , wherein the first portion of the document under test is associated with a first text string and the second portion of the document under test is associated with a second text string.

17 . The system of claim 11 , wherein the first portion of the document under test is associated with a field label and the second portion of the document under test is a text field associated with the field label.

18 . The system of claim 11 , wherein the first measure of blur is determined by applying Canny edge detection to the first portion of the document under test and the second measure of blur is determined by applying Canny edge detection to the second portion of the document under test.

19 . The system of claim 11 , wherein the first measure of blur is determined by applying Laplacian variance detection to the first portion of the document under test and the second measure of blur is determined by applying Laplacian variance to the second portion of the document under test.

20 . The system of claim 11 , wherein the first measure of blur is determined by applying Cepstral techniques to the first portion of the document under test and the second measure of blur is determined by applying Cepstral techniques to the second portion of the document under test.

Assignees

• Jumio Corp

Inventors

• Wells Stuart
• Balogh Attila
• Singh Anshuman Vikram
• Krump Thomas
• Huff Daryl

Classifications

• G06V30/414

  Extracting the geometrical structure, e.g. layout tree; Block segmentation, e.g. bounding boxes for graphics or text · CPC title

• G06V30/1801

  Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes or intersections (extracting features by contour coding G06V30/182) · CPC title

• G06V10/993

  Evaluation of the quality of the acquired pattern · CPC title

• G06V30/10

  Character recognition · CPC title

• G06V30/133 Primary

  Evaluation of quality of the acquired characters · CPC title