IP Library Granted Patent US 11,610,653
Granted Patent B2
US 11,610,653 · App. 15/662,246 · Granted Mar 21, 2023

Systems and methods for improved optical character recognition of health records

Inventors: John O. Schneider (Los Gatos, CA); Vishnuvyas Sethumadhavan (Mountain View, CA); Haoning Fu (Belmont, CA)
Assignee: APIXIO, INC.
G16H10/60G06F16/5846G06Q10/0639G06Q10/10G06Q50/22G16H30/20G16H40/63G16H40/67G06V30/10
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Quick Facts
Patent No.
US 11,610,653
App. No.
15/662,246
Granted
Mar 21, 2023
Kind
B2
Abstract

Systems and methods to improve the optical character recognition of records, and in particular health records, are provided. An image of a medical record is received, and an initial optical image recognition (OCR) on the image is performed to identify text information. The OCR signal quality may be measured, and areas of insufficient OCR signal quality may be isolated. The signal quality is determined by a weighted average of semantic analysis of the resulting text, and/or OCR accuracy measures. The OCR process may be repeated on the isolated regions of lower signal quality, each time using a different OCR transform, until all regions are completed with a desired degree of signal quality (accuracy). All the regions of the document may then be recompiled into a single document for outputting.

Claims (34)

1. A computerized method for generating machine readable documents, the method comprising:

receiving an image;

segmenting the image into multiple continuous portions based upon containing similar attributes;

selecting a first optical image recognition (OCR) transform from a plurality of OCR transforms comprising a weighted average of semantic analysis of text information, a calculated figure of merit equation between characters in identified text information and an example font, and maximizing entropy within identified text information area in a linear model, to analyze each of the continuous portions;

for each of the continuous portions, performing an initial OCR on the image to identify text information using the selected first OCR transform;

measuring an OCR signal quality of the initial OCR;

when the OCR signal quality of the initial OCR of a corresponding continuous portion is sufficient, outputting the text information of the initial OCR; and

when the OCR signal quality of the initial OCR is insufficient, the method further comprises:

selecting a second OCR transform from remaining of the plurality of OCR transforms to perform a second OCR to identify a text information on the corresponding continuous portion;

measuring a signal quality of the second OCR on the corresponding continuous portion; and

if the signal quality of the second OCR of the corresponding continuous portion is sufficient, outputting the text information from the second OCR.

2. The method of claim 1 , wherein the OCR signal quality is determined by OCR accuracy measures.

3. The method of claim 1 , further comprising identifying regions of continuous OCR signal quality within the OCR image.

4. The method of claim 1 , further comprising identifying the transform for each region of contiguous signal that has the highest OCR quality signal.

5. The method of claim 4 , further comprising applying the identified transform to each region of continuous signal to generate a plurality of OCR portions.

6. The method of claim 5 , further comprising recompiling the plurality of OCR portions into a single document.

7. The method of claim 6 , further comprising outputting the single document.

8. A computerized system for generating machine readable documents comprising:

an optical character recognition (OCR) server configured to receive an image, segment the image into continuous portions based upon containing similar attributes, and for each of the continuous portions, selecting a first optical image recognition (OCR) transform from a plurality of OCR transforms comprising a weighted average of semantic analysis of text information, a calculated figure of merit equation between characters in identified text information and an example font, and maximizing entropy within identified text information area in a linear model, to analyze each of the continuous portions, performing an initial OCR on the image to identify text information using the selected first OCR transform;

a statistical modeling server configured to measure an OCR signal quality of the initial OCR;

when the OCR signal quality of the initial OCR of a corresponding continuous portion is sufficient the OCR server is further configured to output the text information of the initial OCR; and

when the OCR signal quality of the initial OCR is insufficient, the OCR server is further configured to select a second OCR transform from remaining of the plurality of OCR transforms to perform a second OCR to identify a text information on the corresponding continuous portion, measure a signal quality of the second OCR on the corresponding continuous portion, and if the signal quality of the second OCR of the corresponding continuous portion is sufficient, output the text information from the second OCR; and

the OCR server is further configured to aggregate the outputted text information from each of the continuous portions.

9. The system of claim 8 , wherein the OCR signal quality is determined by OCR accuracy measures.

10. The system of claim 8 , wherein the OCR server is further configured to identify portions of continuous OCR signal quality within the OCR image.

11. The system of claim 8 , wherein the statistical modeling server is further configured to identifying the transform for each portion of continuous signal that has the highest quality signal.

12. The system of claim 11 , wherein the OCR server is further configured to apply the identified transform to each portion of continuous signal to generate a plurality of OCR portions.

13. The system of claim 12 , wherein the OCR server is further configured to recompile the plurality of OCR portions into a single document.

14. The system of claim 13 , wherein the OCR server is further configured to output the single document.

15. The method of claim 1 further comprising when the OCR signal quality of each of the continuous portions is sufficient, combining the continuous portions into a single document for outputting.

16. The method of claim 1 further comprising when the OCR signal quality of the first OCR and the OCR signal quality of the subsequent OCR are insufficient, iteratively applying an additional OCR transform to the portion with insufficient OCR signal quality, wherein the additional OCR transform is different from the first OCR transform and the subsequent OCR transform and measuring the OCR signal quality of the continuous portion until a sufficient OCR signal quality for the continuous portion is reached.

17. The method of claim 1 further comprising processing the image to determine which OCR transform to use as the first OCR transform.

18. The method of claim 1 , wherein the OCR signal quality for a first continuous portion of the continuous portions is sufficient based on the first OCR transform and the OCR signal quality for a second continuous portion of the continuous portions is sufficient based on the subsequent OCR transform, wherein the first continuous portion and the second continuous portion are different.

19. The method of claim 1 , wherein the first OCR transform is at least one of a font matching OCR transform, a cryptogram solving OCR, and a statistical machine translation OCR, wherein the subsequent OCR transform is at least one of the font matching OCR transform, the cryptogram solving OCR, and the statistical machine translation OCR, and wherein the first OCR transform and the subsequent OCR transform are different.

Assignments (5)
CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNEE NAME PREVIOUSLY RECORDED ON REEL 44033 FRAME 655. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT OF ASSIGNORS INTEREST. Recorded Dec 11, 2024
From: SCHNEIDER, JOHN O.; SETHUMADHAVAN, VISHNUVYAS; FU, HAONING
To: APIXIO INC.
Reel/Frame 069589/0639 →
RELEASE OF SECURITY INTEREST Recorded Aug 30, 2024
From: CHURCHILL AGENCY SERVICES LLC
To: APIXIO, LLC (F/K/A APIXIO INC.)
Reel/Frame 068453/0713 →
ENTITY CONVERSION Recorded Jul 12, 2023
From: APIXIO INC.
To: APIXIO, LLC
Reel/Frame 064259/0006 →
SECURITY INTEREST Recorded Jun 13, 2023
From: APIXIO INC.
To: CHURCHILL AGENCY SERVICES LLC
Reel/Frame 063928/0847 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 3, 2017
From: SCHNEIDER, JOHN O.; SETHUMADHAVAN, VISHNUVYAS; FU, HAONING
To: APIXIO, INC.
Reel/Frame 044033/0655 →
Continuity (6)
Continuation In Part 13223228 · Aug 31, 2011
Continuation In Part 13747336 · Jan 22, 2013
Provisional Application 62369007 · Jul 29, 2016
Provisional Application 61379228 · Sep 1, 2010
Provisional Application 61590330 · Jan 24, 2012
Related Publication 20180011974A1 · Jan 11, 2018
Cited By (1)
US 12,380,972