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The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 12424010 B1

PDFFull Text
Date of Patent:
Sep. 23, 2025

Filed:

Feb. 14, 2023

Character recognition model training method and apparatus, character recognition method and apparatus, device and storage medium

Applicant:

Beijing Baidu Netcom Science Technology Co., Ltd., Beijing, CN;

Inventors:

Pengyuan Lv, Beijing, CN;

Chengquan Zhang, Beijing, CN;

Shanshan Liu, Beijing, CN;

Meina Qiao, Beijing, CN;

Yangliu Xu, Beijing, CN;

Liang Wu, Beijing, CN;

Xiaoyan Wang, Beijing, CN;

Kun Yao, Beijing, CN;

Junyu Han, Beijing, CN;

Errui Ding, Beijing, CN;

Jingdong Wang, Beijing, CN;

Tian Wu, Beijing, CN;

Haifeng Wang, Beijing, CN;

Assignee:

BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD., Beijing, CN;

Attorney:

Tucker Ellis LLP

Primary Examiner:

Henok Shiferaw

Assistant Examiner:

Dennis Rosario

Int. Cl.
CPC ...
G06V 30/19 (2022.01); G06F 7/76 (2006.01); G06F 16/24 (2019.01); G06F 16/33 (2025.01); G06F 16/43 (2019.01); G06F 16/53 (2019.01); G06F 16/583 (2019.01); G06F 16/73 (2019.01); G06F 16/83 (2019.01); G06F 18/21 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/25 (2023.01); G06F 40/258 (2020.01); G06F 40/279 (2020.01); G06N 3/045 (2023.01); G06N 3/0455 (2023.01); G06N 3/0895 (2023.01); G06N 3/09 (2023.01); G06V 10/26 (2022.01); G06V 10/40 (2022.01); G06V 10/42 (2022.01); G06V 10/44 (2022.01); G06V 10/62 (2022.01); G06V 10/70 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); G06V 10/778 (2022.01); G06V 10/80 (2022.01); G06V 20/40 (2022.01); G06V 20/69 (2022.01); G06V 20/70 (2022.01); G06V 30/00 (2022.01); G06V 30/10 (2022.01); G06V 30/148 (2022.01); G06V 30/18 (2022.01); G06V 30/24 (2022.01); G06V 30/242 (2022.01); G06V 30/244 (2022.01); G06V 30/32 (2022.01); G06V 40/12 (2022.01); G06V 40/16 (2022.01); G06V 40/18 (2022.01); G06V 40/30 (2022.01); G10L 15/02 (2006.01); G10L 15/06 (2013.01);
U.S. Cl.
CPC ...
G06V 30/19147 (2022.01); G06F 7/764 (2013.01); G06F 16/24 (2019.01); G06F 16/33 (2019.01); G06F 16/43 (2019.01); G06F 16/53 (2019.01); G06F 16/5846 (2019.01); G06F 16/73 (2019.01); G06F 16/83 (2019.01); G06F 18/21 (2023.01); G06F 18/213 (2023.01); G06F 18/2155 (2023.01); G06F 18/253 (2023.01); G06F 40/258 (2020.01); G06F 40/279 (2020.01); G06N 3/045 (2023.01); G06N 3/0455 (2023.01); G06N 3/0895 (2023.01); G06N 3/09 (2023.01); G06V 10/26 (2022.01); G06V 10/40 (2022.01); G06V 10/42 (2022.01); G06V 10/44 (2022.01); G06V 10/62 (2022.01); G06V 10/70 (2022.01); G06V 10/7715 (2022.01); G06V 10/7753 (2022.01); G06V 10/7784 (2022.01); G06V 10/7788 (2022.01); G06V 10/7792 (2022.01); G06V 10/80 (2022.01); G06V 10/806 (2022.01); G06V 20/46 (2022.01); G06V 20/695 (2022.01); G06V 20/70 (2022.01); G06V 30/00 (2022.01); G06V 30/10 (2022.01); G06V 30/148 (2022.01); G06V 30/15 (2022.01); G06V 30/18 (2022.01); G06V 30/18143 (2022.01); G06V 30/18152 (2022.01); G06V 30/19127 (2022.01); G06V 30/19167 (2022.01); G06V 30/24 (2022.01); G06V 30/242 (2022.01); G06V 30/245 (2022.01); G06V 30/333 (2022.01); G06V 40/1347 (2022.01); G06V 40/1353 (2022.01); G06V 40/1359 (2022.01); G06V 40/168 (2022.01); G06V 40/193 (2022.01); G06V 40/382 (2022.01); G10L 15/02 (2013.01); G10L 15/063 (2013.01); G06F 18/2163 (2023.01); G06F 18/2178 (2023.01); G06F 2218/08 (2023.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20112 (2013.01); G06V 2201/09 (2022.01);
Abstract

The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

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