公开(公告)号：US20230177821A1

公开(公告)日：2023-06-08

申请号：US18063564

申请日：2022-12-08

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Qiming PENG ,  Bin LUO ,  Yuhui CAO ,  Shikun FENG ,  Yongfeng CHEN

IPC: G06V10/82 ,  G06V30/19 ,  G06V30/14

CPC classification number: G06V10/82 ,  G06V30/19147 ,  G06V30/1444

Abstract: A neural network training method and a document image understanding method is provided. The neural network training method includes: acquiring text comprehensive features of a plurality of first texts in an original image; replacing at least one original region in the original image to obtain a sample image including a plurality of first regions and a ground truth label for indicating whether each first region is a replaced region; acquiring image comprehensive features of the plurality of first regions; inputting the text comprehensive features of the plurality of first texts and the image comprehensive features of the plurality of first regions into a neural network model together to obtain text representation features of the plurality of first texts; determining a predicted label based on the text representation features of the plurality of first texts; and training the neural network model based on the ground truth label and the predicted label.

公开(公告)号：US20220382991A1

公开(公告)日：2022-12-01

申请号：US17883908

申请日：2022-08-09

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Qiming PENG ,  Bin LUO ,  Yuhui CAO ,  Shikun FENG ,  Yongfeng CHEN

IPC: G06F40/30 ,  G06V30/414 ,  G06V30/14

Abstract: The present disclosure provides a training method and apparatus for a document processing model, a device, a storage medium and a program, which relate to the field of artificial intelligence, and in particular, to technologies such as deep learning, natural language processing and text recognition. The specific implementation is: acquiring a first sample document; determining element features of a plurality of document elements in the first sample document and positions corresponding to M position types of each document element according to the first sample document; where the document element corresponds to a character or a document area in the first sample document; and performing training on a basic model according to the element features of the plurality of document elements and the positions corresponding to the M position types of each document element to obtain the document processing model.