# US 10,872,266 B1 · Handwriting recognition systems and methods

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 10,872,266 B1
- Document ID: US10872266B1
- Status: Granted
- Kind: B1
- Application number: 16/925,099
- Filed: 2020-07-09
- Granted: 2020-12-22
- Earliest claimed: 2019-05-22
- Assignees: Read-Ink Corporation
- Applicants: Read-Ink Corporation
- Inventors: Thomas O Binford
- Web page: https://patentgrep.com/patent/US10872266B1

## CPC

- G06V 30/347: {Sampling; Contour coding; Stroke extraction}
- G06F 18/29: {Graphical models, e.g. Bayesian networks}
- G06T 7/60: Analysis of geometric attributes
- G06V 10/426: Graphical representations
- G06V 10/44: Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V 10/751: {Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching}
- G06V 30/153: {using recognition of characters or words}
- G06V 30/268: {Lexical context}
- G06V 30/36: {Matching; Classification}
- G06T 2207/30176: Document
- G06V 30/10: Character recognition
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

Systems and methods are provided for handwriting recognition. Handwriting data is received. Geometric data of text in handwriting data is determined. Sub-characters of the text are determined. Sub-characters of text are matched to a model. Most probable characters of the text are determined based on the matching.

## Claims

### Claim 1

A method for recognizing handwriting, the method comprising:
receiving handwriting data;
determining geometric data of text in handwriting data;
determining sub-characters of the text;
matching sub-characters of text to a model; and
determining most probable characters of the text based on said matching,
wherein the matching sub-characters of text to a model comprises:
estimating empirical conditional probability P(dlh) of parameter data based on hypothesis from order statistics of a parameter;
using signed functional distance that is not scalar; and
estimating non-parameterized empirical probability over stratified character and sub-character populations.