IP Library › Granted Patent US 12,444,220
Granted Patent B2
US 12,444,220 · App. 17/782,683 · Granted Oct 14, 2025

Character segmentation method and device based on edge detection and contour detection

Inventors: Chenghai Huo (Beijing, CN); Nangeng Zhang (Beijing, CN)
Assignee: CANAAN BRIGHT SIGHT CO., LTD
G06V30/1801G06V30/148G06V30/18019G06V30/182
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,444,220
App. No.
17/782,683
Granted
Oct 14, 2025
Kind
B2
Abstract

A character segmentation method and apparatus, and a computer-readable storage medium are provided. The method includes converting a character area image into a grayscale image; converting the grayscale image into an edge binary image by edge detection; acquiring character box segmentation blocks from the edge binary image by projection; and determining a target character area from the character box segmentation blocks by contour detection, and performing character segmentation on the character area image according to the target character area; or comprises: converting a character area image into a grayscale image; performing clustering analysis on the grayscale image by fuzzy C-means clustering, and binarizing the grayscale image according to the analysis result; acquiring character positioning blocks from a binary image by projection; and performing character segmentation on the character area image according to position information of the character positioning blocks. Character segmentation can be performed on a relatively low quality image.

Claims (22)

1. A character segmentation method, comprising:

acquiring a character region image and converting the character region image into a grayscale image, wherein the character region image comprises at least one character frame;

adaptively adjusting parameters of an edge detection algorithm based on image quality characteristics of the grayscale image, and converting the grayscale image into an edge binary image by utilizing the edge detection algorithm;

acquiring, from the edge binary image, at least one segment for the at least one character frame by utilizing a projection approach;

determining a character contour in each of the at least one segment by utilizing a contour detection algorithm; and

determining a minimum enclosing rectangle region of the character contour in each of the at least one segment and performing character segmentation on the character region image based on the minimum enclosing rectangle region.

2. The character segmentation method according to claim 1 , further comprising, prior to converting the grayscale image into the edge binary image by utilizing the edge detection algorithm:

performing median filtering and/or Gaussian filtering on the grayscale image.

3. The character segmentation method according to claim 1 , wherein utilizing the edge detection algorithm comprises utilizing a Canny operator.

4. The character segmentation method according to claim 1 , wherein the image quality characteristics comprise at least one of a global contrast or a global grayscale mean value.

5. The character segmentation method according to claim 1 , wherein utilizing the projection approach comprises:

performing segmentation on the edge binary image by a vertical projection to acquire the at least one segment for the at least one character frame.

6. A character segmentation device, comprising:

an acquisition module configured to acquire a character region image and convert the character region image into a grayscale image, wherein the character region image comprises at least one character frame;

an edge detection module configured to adaptively adjust parameters of an edge detection algorithm based on image quality characteristics of the grayscale image, and convert the grayscale image into an edge binary image by utilizing the edge detection algorithm;

a projection module configured to acquire, from the edge binary image, at least one segment for the at least one character frame by utilizing a projection approach; and

a contour detection module configured to determine a character contour in each of the at least one segment by utilizing a contour detection algorithm, and determine a minimum enclosing rectangle region of the character contour in each of the at least one segment and perform character segmentation on the character region image based on the minimum enclosing rectangle region.

7. A character segmentation device, comprising:

one or more multicore processors; and

a memory having one or more programs stored therein,

wherein the one or more programs, when executed by the one or more multicore processors, cause the one or more multicore processors to implement the character segmentation method according to claim 1 .

8. A non-transitory computer-readable storage medium having programs stored thereon, wherein the programs, when executed by a multicore processor, cause the multicore processor to implement the character segmentation method according to claim 1 .

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 7, 2025
From: HUO, CHENGHAI; ZHANG, NANGENG
To: CANAAN BRIGHT SIGHT CO., LTD
Reel/Frame 069773/0644 →
Priority Claims (2)
CN 201911233988.5 · Dec 5, 2019 · national
CN 201911234826.3 · Dec 5, 2019 · national
Continuity (1)
Related Publication 20230009564A1 · Jan 12, 2023
References Cited (35)
US 7302098B2 · Tang · 2007 [cited by examiner]
US 8509534B2 · Galic · 2013 [cited by examiner]
US 9158986B2 · Nakamura · 2015 [cited by examiner]
US 9305224B1 · Chen · 2016 [cited by examiner]
US 9324001B2 · Nakamura · 2016 [cited by examiner]
US 9959487B2 · Gao · 2018 [cited by examiner]
US 10095925B1 · Tripuraneni · 2018 [cited by examiner]
US 10922572B2 · Gao · 2021 [cited by examiner]
US 20060120602A1 · Tang et al. · 2006 [cited by applicant]
US 20130259383A1 · Kondo et al. · 2013 [cited by applicant]
US 20150039637A1 · Neuhauser et al. · 2015 [cited by applicant]
US 20180068196A1 · Quentin et al. · 2018 [cited by applicant]
CN 102426649A · 2012 [cited by applicant]
CN 104156704A · 2014 [cited by applicant]
CN 105373794A · 2016 [cited by applicant]
CN 105825212A · 2016 [cited by applicant]
CN 109522889A · 2019 [cited by applicant]
CN 109543688A · 2019 [cited by applicant]
CN 109598271A · 2019 [cited by applicant]
CN 109726717A · 2019 [cited by applicant]
CN 110097046A · 2019 [cited by examiner]
CN 111027546A · 2020 [cited by applicant]
CN 111046862A · 2020 [cited by applicant]
CN 113378847B · 2022 [cited by examiner]
CN 115984863B · 2023 [cited by examiner]
JP 8264591A · 1996 [cited by applicant]
JP H1196292A · 1999 [cited by applicant]
JP 2013210785A · 2013 [cited by applicant]
JP 201727201A · 2017 [cited by applicant]
EPO Extended European search report & search opinion in European Patent Application No. 20895306.7 (Year: 2023). [cited by examiner]
JPO Notice of Reasons for Refusal in Japanese Patent Application No. 2024-018589 (Year: 2024). [cited by examiner]
JPO Notice of Reasons for Refusal in Japanese Patent Application No. 2022-533643 (Year: 2023). [cited by examiner]
Hongxi Wei et al., “An Efficient Binarization Method for Ancient Mongolian Document Images”, 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE). [cited by applicant]
Christos-Nikolaos E. Anagnostopoulos et al., “License Plate Recognition From Still Images and Video Sequences: A Survey”, IEEE Transactions on Intelligent Transportation Systems, vol. 9, No. 3, Sep. 2008, pp. 377-391. [cited by applicant]
Hideo Kasuga et al., “Fuzzy Clustering for Text Extract from Color Image”, Faculty of Engineering, Shinshu University, 1999. [cited by applicant]