Method and system for evaluating quality of a document

Patent metadata

Field	Value
Publication number	US-2024177285-A1
Application number	US-202318120363-A
Country	US
Kind code	A1
Filing date	Mar 11, 2023
Priority date	Nov 28, 2022
Publication date	May 30, 2024
Grant date	—

Abstract

A method and system of determining quality of a document image is disclosed that includes segmenting, by one or more processors, a document image into a plurality of regions each of which comprises text data. The plurality of regions is classified into one of a plurality of image quality classes based on a determination of a highest prediction value from one of a plurality of machine learning models. The plurality of machine learning models is trained corresponding to one of the plurality of image quality classes. A cumulative quality score for the image is computed based on a weighted average of a number of regions classified into each of the plurality of image quality classes. The quality of the image is determined based on the cumulative quality score.

First claim

What is claimed is:

1 . A method of determining quality of a document image, the method comprising:
segmenting, by a computing device, the document image into a plurality of regions, wherein each of the plurality of regions comprises text data; classifying, by the computing device, each of the plurality of regions into one of a plurality of image quality classes,
wherein each of the plurality of regions is classified based on a determination of a highest prediction value from one of a plurality of machine learning models, and
wherein each of the plurality of machine learning models is trained corresponding to one of the plurality of image quality classes;
computing, by the computing device, a cumulative quality score for the document image based on a weighted average of a number of regions classified into each of the plurality of image quality classes; and determining, by the computing device, the quality of the document image based on the cumulative quality score.

2 . The method of claim 1 , wherein the training of each of the plurality of machine learning models corresponding to one of the plurality of image quality classes comprises determining a training dataset for each of the plurality of image quality classes.

3 . The method of claim 2 , wherein the determination of the training dataset for each of the plurality of image quality classes comprises:
segmenting, by a computing device, a training image into a plurality of regions, wherein each of the plurality of regions comprises text data; for each of the plurality of regions,
performing, by the computing device, optical character recognition (OCR) using two or more OCR systems to determine corresponding two or more OCR text data;
determining, by the computing device, text matching scores based on a comparison among the two or more OCR text data using a plurality of string matching techniques;
determining, by the computing device, a plurality of threshold values for the plurality of image quality classes based on a statistical analysis of the text matching scores based on the plurality of string matching techniques and for the plurality of regions; and clustering, by the computing device, the plurality of regions into one of the plurality of image quality classes based on the plurality of threshold values.

4 . The method of claim 3 , wherein the determination of the plurality of threshold values comprises:
computing, for each of the plurality of regions, a minimum text matching score, a maximum text matching score, and an average text matching score based on the text matching scores for the plurality of string matching techniques; and determining the plurality of threshold values based on a statistical calculation of the minimum, the maximum, and the average text matching scores for the plurality of regions.

5 . The method of claim 3 , wherein
the plurality of threshold values comprises a lower threshold value and an upper threshold value, and wherein the plurality of image quality classes comprises a bad image quality class for document images for the average text matching score less than or equal to the lower threshold value, a good image quality class for the average text matching score greater than or equal to the upper threshold value, and a medium image quality class for the average text matching score greater than the lower threshold value and less than the upper threshold value.

6 . The method of claim 1 , wherein the plurality of regions comprises at least one of a region with word level text data, a region with sentence level text data, a region with paragraph level text data, and a region with page level text data.

7 . The method of claim 1 , wherein the computing device is configured to determine quality of a document comprising a plurality of document images by:
computing a cumulative quality score for each of the plurality of document images based on the weighted average of the number of regions classified into each of the plurality of image quality classes; and determining the quality of the document based on the cumulative quality score for each of the plurality of document images.

8 . The method of claim 1 , wherein the computing device is configured to re-train the plurality of machine learning models based on a variance in a prediction value from each of the plurality of machine learning models.

9 . A system for determining quality of a document image, comprising:
one or more processors; a memory communicatively coupled to the processors, wherein the memory stores a plurality of processor-executable instructions, which, upon execution, cause the processors to: segment the document image into a plurality of regions, wherein each of the plurality of regions comprises text data; classify each of the plurality of regions into one of a plurality of image quality classes, wherein each of the plurality of regions is classified based on a determination of a highest prediction value from one of a plurality of machine learning models, and wherein each of the plurality of machine learning models is trained corresponding to one of the plurality of image quality classes; compute a cumulative quality score for the document image based on a weighted average of a number of regions classified into each of the plurality of image quality classes; and determine the quality of the document image based on the cumulative quality score.

10 . The system of claim 9 , wherein the training of each of the plurality of machine learning models corresponding to one of the plurality of image quality classes comprises determining a training dataset for each of the plurality of image quality classes.

11 . The system of claim 9 , wherein the one or more processors are further configured to determine the training dataset for each of the plurality of image quality classes by:
segmenting, by a computing device, a training image into a plurality of regions, wherein each of the plurality of regions comprises text data; for each of the plurality of regions,
performing optical character recognition (OCR) using two or more OCR systems to determine corresponding two or more OCR text data;
determining text matching scores based on a comparison among the two or more OCR text data using a plurality of string matching techniques;
determining a plurality of threshold values for the plurality of image quality classes based on a statistical analysis of the text matching scores for the plurality of string matching techniques and for the plurality of regions; and
clustering the plurality of regions into one of the plurality of image quality classes based on the plurality of threshold values.

12 . The system of claim 11 , wherein the determination of the plurality of threshold values comprises:
computing, for each of the plurality of regions, a minimum text matching score, a maximum text matching score, and an average text matching score based on the text matching scores for the plurality of string matching techniques; and determining the plurality of threshold values based on a statistical calculation of the minimum, the maximum, and the average text matching scores for the plurality of regions.

13 . The system of claim 11 , wherein
the plurality of threshold values comprises a lower threshold value and an upper threshold value, and wherein the plurality of image quality classes comprises a bad image quality class for document images for the average text matching score less than or equal to the lower threshold value, a good image quality class for the average text matching score greater than or equal to the upper threshold value, and a medium image quality class

Assignees

• L&T Technology Services Ltd

Inventors

• Das Tarun Kumar
• Mallick Triptesh
• Singh Madhusudan
• Kumar Pragyesh
• Balaraman Mridul

Classifications

• G06T7/0002 Primary

  Inspection of images, e.g. flaw detection · CPC title

• G06T7/11

  Region-based segmentation · CPC title

• G06V10/762

  using clustering, e.g. of similar faces in social networks · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06V30/19093

  Proximity measures, i.e. similarity or distance measures · CPC title