Reading and recognizing handwritten characters to identify names using neural network techniques

Patent metadata

Field	Value
Publication number	US-12106591-B2
Application number	US-202217661647-A
Country	US
Kind code	B2
Filing date	May 2, 2022
Priority date	May 2, 2022
Publication date	Oct 1, 2024
Grant date	Oct 1, 2024

Abstract

A system and method for identifying handwritten characters on an image using a classification model that employs a neural network. The system includes a computer having a processor and a memory device that stores data and executable code that, when executed, causes the processor to read and convert typed text on the image to machine encoded text to identify locations of the typed text on the image; identify a location on the image that includes handwritten text based on the location of predetermined typed text on the image; identify clusters of non-white pixels in the image at the location having the handwritten text; generate an individual and separate cluster image for each identified cluster; classify each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character; and determine what character each cluster image is based on the classification.

First claim

What is claimed is:

1. A system for identifying handwritten characters on an image, said system comprising:
a computer including at least one processor and a memory device storing data and executable code that, when executed, causes the at least one processor to:
read and convert typed text on the image to machine encoded text to identify locations of the typed text on the image;
identify a location on the image that includes handwritten text based on the location of predetermined typed text on the image;
identify clusters of non-white pixels in the image at the location having the handwritten text, where each cluster is presumed to be a handwritten character;
generate an individual and separate cluster image for each identified cluster, wherein the at least one processor rescales the cluster when it generates an individual and separate cluster image for each identified cluster;
classify each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character; and
determine what character each cluster image is based on the classification.

2. The system according to claim 1 wherein the at least one processor reads and converts the typed text using an optical character recognition algorithm.

3. The system according to claim 1 wherein the at least one processor identifies clusters of non-white pixels using a density-based clustering algorithm.

4. The system according to claim 1 wherein the at least one processor rescales the cluster to a 28×28 pixel cluster image.

5. The system according to claim 1 wherein the at least one processor centers the cluster in the cluster image when it generates an individual and separate cluster image for each identified cluster.

6. The system according to claim 1 wherein the at least one processor classifies each cluster image by determining the likelihood that the cluster image is one of sixty-two character classes, namely, upper case letters A-Z, lower case letters a-z and numbers 0-9.

7. The system according to claim 1 wherein the at least one neural network is a convolutional neural network (CNN).

8. The system according to claim 1 wherein the image is an image of a check, the predetermined typed text is “pay to the order of” and the handwritten text is a payee on the check.

9. A system for identifying handwritten characters on an image of a check, said system comprising:
a computer including at least one processor and a memory device storing data and executable code that, when executed, causes the at least one processor to:
read and convert typed text on the image to machine encoded text using an optical character recognition algorithm to identify locations of the typed text on the image including identifying the location of the typed words “pay to the order of”;
identify a location on the image that includes a handwritten payee based on the location of the typed words “pay to the order of”;
identify clusters of non-white pixels in the image at the location having the handwritten payee using a density-based clustering algorithm, where each cluster is presumed to be a handwritten character;
generate an individual and separate cluster image for each identified cluster, wherein the at least one processor rescales the cluster to a 28×28 pixel cluster image;
classify each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character, wherein the at least one processor classifies each cluster image by determining the likelihood that the cluster image is one of sixty-two character classes, namely, upper case letters A-Z, lower case letters a-z and numbers 0-9; and
determine what character each cluster image is based on the classification.

10. A method for identifying handwritten characters on an image, said method comprising:
reading and converting typed text on the image to machine encoded text to identify locations of the typed text on the image;
identifying a location on the image that includes handwritten text based on the location of predetermined typed text on the image;
identifying clusters of non-white pixels in the image at the location having the handwritten text, where each cluster is presumed to be a handwritten character;
generating an individual and separate cluster image for each identified cluster, wherein generating an individual and separate cluster image for each identified cluster includes rescaling each cluster;
classifying each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character; and
determining what character each cluster image is based on the classification.

11. The method according to claim 10 wherein reading and converting the typed text includes using an optical character recognition algorithm.

12. The method according to claim 10 wherein identifying clusters of non-white pixels includes using a density-based clustering algorithm.

13. The method according to claim 10 wherein each cluster is rescaled to a 28×28 pixel cluster image.

14. The method according to claim 10 wherein generating an individual and separate cluster image for each identified cluster includes centering the cluster in the image.

15. The method according to claim 10 wherein the at least one processor classifies each cluster image by determining the likelihood that the cluster image is one of sixty-two character classes, namely, upper case letters A-Z, lower case letters a-z and numbers 0-9.

16. The method according to claim 10 wherein the at least one neural network is a convolutional neural network (CNN).

17. The method according to claim 10 wherein the image is an image of a check, the predetermined typed text is “pay to the order of” and the handwritten text is a payee on the check.

18. A system for identifying handwritten characters on an image, said system comprising:
a computer including at least one processor and a memory device storing data and executable code that, when executed, causes the at least one processor to:
read and convert typed text on the image to machine encoded text to identify locations of the typed text on the image;
identify a location on the image that includes handwritten text based on the location of predetermined typed text on the image;
identify clusters of non-white pixels in the image at the location having the handwritten text, where each cluster is presumed to be a handwritten character;
generate an individual and separate cluster image for each identified cluster, wherein the at least one processor centers the cluster in the cluster image when it generates an individual and separate cluster image for each identified cluster;
classify each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character; and
determine what character each cluster image is based on the classification.

19. A method for identifying handwritten characters on an image, said method comprising:
reading and converting typed text on the image to machine encoded text to identify locations of the typed text on the image;
identifying a location on the image that includes handwritten text based on the location of predetermined typed text on the image;
identifying clusters of non-white pixels in the image at the location having the handwritten text, where each cluster is presumed to be a handwritten character;
generating an individual and separate cluster image for each i

Assignees

• Truist Bank

Inventors

• Fitzgerald Raphael

Classifications

• G06V10/82

  using neural networks · CPC title

• G06F40/126

  Character encoding · CPC title

• G06V30/1452

  based on positionally close symbols, e.g. amount sign or URL-specific characters · CPC title

• G06V30/42

  based on the type of document · CPC title

• G06T3/4046

  using neural networks · CPC title