Detecting fields in document images

Patent metadata

Field	Value
Publication number	US-2025329187-A1
Application number	US-202519256919-A
Country	US
Kind code	A1
Filing date	Jul 1, 2025
Priority date	Jul 21, 2021
Publication date	Oct 23, 2025
Grant date	—

Abstract

A method of detecting fields in document images includes: receiving, by a processing device, a codebook comprising a set of visual words, each visual word corresponding to a center of a cluster of local descriptors, wherein each local descriptor is associated with a respective keypoint region of a first set of document images; calculating, based on a second set of document images, for each visual word of the codebook, a respective frequency distribution of a field position of a specified field with respect to the visual word; loading a document image for extraction of target fields; and detecting fields in the document image based on the calculated frequency distributions.

First claim

What is claimed is:

1 . A method, comprising:
receiving, by a processing device, a codebook comprising a set of visual words, each visual word corresponding to a center of a cluster of local descriptors, wherein each local descriptor is associated with a respective keypoint region of a first set of document images; calculating, based on a second set of document images, for each visual word of the codebook, a respective frequency distribution of a field position of a specified field with respect to the visual word; loading a document image for extraction of target fields; and detecting fields in the document image based on the calculated frequency distributions.

2 . The method of claim 1 , wherein the codebook is optimized on a third set of document images.

3 . The method of claim 1 , wherein calculating the respective frequency distribution comprises calculating an integral two-dimensional histogram of shift of a position of the specified field, and wherein the integral two-dimensional histogram incorporates a plurality of shifts relative to possible positions of each visual word.

4 . The method of claim 1 , wherein detecting fields in the document image further comprises:
obtaining an accumulated distribution histogram based on possible positions of the target field with respect to two or more visual words of the set of visual words.

5 . The method of claim 1 , wherein a plurality of document images of the second set of document images have a similar layout.

6 . The method of claim 1 , further comprising:
dividing the second set of document images into groups based on document similarity prior to at least one of: training a model or using the model.

7 . The method of claim 1 , further comprising:
extracting the respective keypoint region by morphologically preprocessing each document image of the first set of document images.

8 . A system, comprising:
a memory; and a processing device coupled to the memory, the processing device configured to:
receive a codebook comprising a set of visual words, each visual word corresponding to a center of a cluster of local descriptors, wherein each local descriptor is associated with a respective keypoint region of a first set of document images;
calculate, based on a second set of document images, for each visual word of the codebook, a respective frequency distribution of a field position of a specified field with respect to the visual word;
load a document image for extraction of target fields; and
detect fields in the document image based on the calculated frequency distributions.

9 . The system of claim 8 , wherein the codebook is optimized on a third set of document images.

10 . The system of claim 8 , wherein calculating the respective frequency distribution comprises calculating an integral two-dimensional histogram of shift of a position of the specified field, and wherein the integral two-dimensional histogram incorporates a plurality of shifts relative to possible positions of each visual word.

11 . The system of claim 8 , wherein detecting fields in the document image further comprises:
obtaining an accumulated distribution histogram based on possible positions of the target field with respect to two or more visual words of the set of visual words.

12 . The system of claim 8 , wherein a plurality of document images of the second set of document images have a similar layout.

13 . The system of claim 8 , wherein the processing device is further configured to:
divide the second set of document images into groups based on document similarity prior to at least one of: training a model or using the model.

14 . The system of claim 8 , wherein the processing device is further configured to:
extract the respective keypoint region by morphologically preprocessing each document image of the first set of document images.

15 . A non-transitory computer-readable storage medium comprising executable instructions that, when executed by a processing device, cause the processing device to:
receive a codebook comprising a set of visual words, each visual word corresponding to a center of a cluster of local descriptors, wherein each local descriptor is associated with a respective keypoint region of a first set of document images; calculate, based on a second set of document images, for each visual word of the codebook, a respective frequency distribution of a field position of a specified field with respect to the visual word; load a document image for extraction of target fields; and detect fields in the document image based on the calculated frequency distributions.

16 . The non-transitory computer-readable storage medium of claim 15 , wherein the codebook is optimized on a third set of document images.

17 . The non-transitory computer-readable storage medium of claim 15 , wherein calculating the respective frequency distribution comprises calculating an integral two-dimensional histogram of shift of a position of the specified field, and wherein the integral two-dimensional histogram incorporates a plurality of shifts relative to possible positions of each visual word.

18 . The non-transitory computer-readable storage medium of claim 15 , wherein detecting fields in the document image further comprises:
obtaining an accumulated distribution histogram based on possible positions of the target field with respect to two or more visual words of the set of visual words.

19 . The non-transitory computer-readable storage medium of claim 15 , wherein a plurality of document images of the second set of document images have a similar layout.

20 . The non-transitory computer-readable storage medium of claim 15 , further comprising executable instructions that, when executed by the processing device, cause the processing device to:
divide the second set of document images into groups based on document similarity prior to at least one of: training a model or using the model.

Assignees

• Abbyy Dev Inc

Inventors

• Zagaynov Ivan
• Loginov Vasily
• Semenov Stanislav
• Valiukov Aleksandr

Classifications

• G06V30/18152 Primary

  Extracting features based on a plurality of salient regional features, e.g. "bag of words" · CPC title

• G06V10/462

  Salient features, e.g. scale invariant feature transforms [SIFT] · CPC title

• G06V30/18143

  Extracting features based on salient regional features, e.g. scale invariant feature transform [SIFT] keypoints · CPC title

• G06F18/2163

  Partitioning the feature space · CPC title

• G06F18/22

  Matching criteria, e.g. proximity measures · CPC title