US 11,756,170 B2
Method and apparatus for correcting distorted document image
Qunyi Xie, Beijing (CN); Xiameng Qin, Beijing (CN); Yulin Li, Beijing (CN); Junyu Han, Beijing (CN); and Shengxian Zhu, Beijing (CN)
Assigned to BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.
Filed by BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD., Beijing (CN)
Filed on Jan. 19, 2021, as Appl. No. 17/151,783.
Claims priority of application No. 202010066508.7 (CN), filed on Jan. 20, 2020.
Prior Publication US 2021/0192696 A1, Jun. 24, 2021
Int. Cl. G06T 5/00 (2006.01); G06N 3/08 (2023.01); G06T 5/30 (2006.01)
CPC G06T 5/006 (2013.01) [G06N 3/08 (2013.01); G06T 5/30 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30176 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A method for correcting a distorted document image, comprising:
obtaining a distorted document image; and
inputting the distorted document image into a correction model, and obtaining a corrected image corresponding to the distorted document image; wherein the correction model is a model obtained by training with a set of image samples as inputs and a corrected image corresponding to each image sample in the set of image samples as an output, and the image samples are distorted,
wherein the correction model comprises a deformation parameter prediction module and a deformation correction module connected in series; wherein the deformation parameter prediction module is a U-shaped convolutional neural network model obtained by training with the set of image samples as inputs and a deformation parameter of each pixel of each image sample comprised in the set of image samples as an output, and the deformation correction module is a model obtained by training with the set of image samples and output results of the deformation parameter prediction module as inputs and the corrected image corresponding to each image sample in the set of image samples as an output;
the inputting the distorted document image into the correction model, and obtaining the corrected image corresponding to the distorted document image comprises:
inputting the distorted document image into the correction model, outputting an intermediate result through the deformation parameter prediction module, and obtaining, according to the intermediate result, the corrected image corresponding to the distorted document image through the deformation correction module; the intermediate result comprising a deformation parameter of each pixel in the distorted document image;
wherein the deformation parameter prediction module comprises at least two stages of deformation parameter prediction sub-modules connected in series; wherein a first-stage deformation parameter prediction sub-module is a U-shaped convolutional neural network model obtained by training with the set of image samples as inputs and a deformation parameter of each pixel of each image sample comprised in the set of image samples as an output, and another stage deformation parameter prediction sub-module is a U-shaped convolutional neural network model obtained by training with the set of image samples and output results of a previous deformation parameter prediction sub-module as inputs and a deformation parameter of each pixel of each image sample comprised in the set of image samples as an output;
the intermediate result is an output result of a last-stage deformation parameter prediction sub-module of the at least two stages of deformation parameter prediction sub-modules.