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.

Date of Patent:
Jan. 23, 2024

Filed:

Oct. 15, 2021
Applicants:

Tsinghua University, Beijing, CN;

Hyundai Motor Company, Seoul, KR;

Kia Corporation, Seoul, KR;

Inventors:

Liangrui Peng, Beijing, CN;

Ruijie Yan, Beijing, CN;

Shanyu Xiao, Beijing, CN;

Gang Yao, Beijing, CN;

Shengjin Wang, Beijing, CN;

Jaesik Min, Gyeonggi-do, KR;

Jong Ub Suk, Seoul, KR;

Assignees:

TSINGHUA UNIVERSITY, Beijing, CN;

HYUNDAI MOTOR COMPANY, Seoul, KR;

KIA CORPORATION, Seoul, KR;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 20/00 (2022.01); G06V 20/62 (2022.01); G06V 10/40 (2022.01); G06V 10/94 (2022.01); G06F 18/213 (2023.01); G06N 3/045 (2023.01); G06V 30/10 (2022.01);
U.S. Cl.
CPC ...
G06V 20/62 (2022.01); G06F 18/213 (2023.01); G06N 3/045 (2023.01); G06V 10/40 (2022.01); G06V 10/95 (2022.01); G06V 30/10 (2022.01);
Abstract

A method of multi-directional scene text recognition based on multi-element attention mechanism include: performing normalization processing for a text row/column image output from an external text detection module by a feature extractor, extracting a feature for the normalized image by using a deep convolutional neural network to acquire an initial feature map, adding a 2-dimensional directional positional encoding P to the initial feature map in order to output a multi-channel feature map, converting the multi-channel feature map output from a feature extractor by an encoder into a hidden representation, and converting the hidden representation output from the encoder into a recognized text by a decoder and using the recognized text as the output result.


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