公开(公告)号：US20230048495A1

公开(公告)日：2023-02-16

申请号：US17974183

申请日：2022-10-26

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

Inventor： Qunyi XIE ,  Xiameng QIN ,  Mengyi EN ,  Dongdong ZHANG ,  Ju HUANG ,  Yangliu XU ,  Yi CHEN ,  Kun YAO

IPC: G06V30/413 ,  G06V10/764 ,  G06V10/24 ,  G06V10/75 ,  G06V30/414

Abstract: A method and a platform of generating a document, an electronic device, and a storage medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision and deep learning technologies, and may be applied to a text recognition scenario and other scenarios. The method includes: performing a category recognition on a document picture to obtain a target category result; determining a target structured model matched with the target category result; and performing, by using the target structured model, a structure recognition on the document picture to obtain a structure recognition result, so as to generate an electronic document based on the structure recognition result, wherein the structure recognition result includes a field attribute recognition result and a field position recognition result.

公开(公告)号：US20230134615A1

公开(公告)日：2023-05-04

申请号：US18146839

申请日：2022-12-27

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

Inventor： Qunyi XIE ,  Dongdong ZHANG ,  Xiameng QIN ,  Mengyi EN ,  Yangliu XU ,  Yi CHEN ,  Ju HUANG ,  Kun YAO

IPC: G06F9/48 ,  G06F40/205 ,  G06F9/50

Abstract: A method of processing a task, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence, in particular to fields of deep learning and computer vision, and may be applied to OCR optical character recognition and other scenarios. The method includes: parsing labeled data to be processed according to a task type identification, to obtain task labeled data, a tag information of the task labeled data is matched with the task type identification, and the task labeled data includes first task labeled data and second task labeled data; training a model using the first task labeled data, to obtain candidate models, the model is determined according to the task type identification; and determining a target model from the candidate models according to a performance evaluation result obtained by performing performance evaluation on the plurality of candidate models using the second task labeled data.