Abstract: Embodiments of the present disclosure provide a method and apparatus for correcting a distorted document image, where the method for correcting a distorted document image includes: 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; where 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. By inputting the distorted document image to be corrected into the correction model, the corrected image corresponding to the distorted document image can be obtained through the correction model, which realizes document image correction end-to-end, improves accuracy of the document image correction, and extends application scenarios of the document image correction.

Abstract: A computer-implemented method includes: acquiring training data, the training data includes training images for a preset vertical type, and the training images include a first training image containing real data of the preset vertical type and a second training image containing virtual data of the preset vertical type ; building a basic model, the basic model includes a deep learning network, and the deep learning network is configured to recognize the training images to extract text data in the training image; and training the basic model by using the training data to obtain the image recognition model.

Abstract: A method, an electronic device and a storage medium for expression driving are disclosed. The method may include: performing facial key point detection on a driven character in a first image to obtain a first facial key point sequence; performing the following processing for each second image of a plurality of second images obtained successively: performing facial key point detection on a driving character in the second image to obtain a second facial key point sequence; obtaining a difference between the second facial key point sequence and an expressionless key point sequence which has been determined previously according to an analysis on the second facial key point sequence for a previous second image, and performing expression drive rendering on the driven character based on the difference and the first facial key point sequence. The technical solution may enhance flexibility, interactivity, accuracy etc.

Abstract: Embodiments of the present disclosure provide a method and apparatus for correcting a distorted document image, where the method for correcting a distorted document image includes: 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; where 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. By inputting the distorted document image to be corrected into the correction model, the corrected image corresponding to the distorted document image can be obtained through the correction model, which realizes document image correction end-to-end, improves accuracy of the document image correction, and extends application scenarios of the document image correction.

Abstract: A method, an electronic device and a storage medium for expression driving are disclosed. The method may include: performing facial key point detection on a driven character in a first image to obtain a first facial key point sequence; performing the following processing for each second image of a plurality of second images obtained successively: performing facial key point detection on a driving character in the second image to obtain a second facial key point sequence; obtaining a difference between the second facial key point sequence and an expressionless key point sequence which has been determined previously according to an analysis on the second facial key point sequence for a previous second image, and performing expression drive rendering on the driven character based on the difference and the first facial key point sequence. The technical solution may enhance flexibility, interactivity, accuracy etc.