Huawei Technologies Co., Limited

Miao Xie

Nan Wang

Qing Zhang

Haibo Chen

Shanxi Chen

Xiang Xu

Junwei Gou

Hengzhi Yao

Shangling Jui

Yingliang Yao

Liehang Shi

Chao Yu

A text recognition method includes scaling a to-be-recognized image based on a first scale ratio, determining first coordinate information corresponding to a text line area in the scaled to-be-recognized image, determining, based on the first scale ratio, second coordinate information corresponding to the first coordinate information, where the second coordinate information is coordinate information of the text line area in the to-be-recognized image, performing character recognition on text line images corresponding to the second coordinate information by using a recognition model, and determining text line content corresponding to the text line images, where the to-be-recognized image includes the text line images.

Text recognition method and terminal device

A security control method and a computer system are provided. A first domain and a second domain are deployed in the computer system, the second domain is more secure than the first domain, a program is deployed in the first domain, and a control flow management module and an audit module are deployed in the second domain. The second domain is more secure than the first domain. When the program in the first domain is executed, the control flow management module obtains control flow information by using a tracer. The audit module audits the to-be-audited information according to an audit rule, and when the to-be-audited information matches the audit rule, determines that the audit succeeds and then allows the first domain to perform a subsequent operation, for example, to access a secure program in the second domain.

Security control method and computer system

This application provides an image aesthetic processing method and an electronic device. A method for generating an image aesthetic scoring model includes: constructing a first neural network based on a preset convolutional structure set; obtaining an image classification neural network, where the image classification neural network is used to classify image scenarios; obtaining a second neural network based on the first neural network and the image classification neural network, where the second neural network is a neural network containing scenario information; and determining an image aesthetic scoring model based on the second neural network, where output information of the image aesthetic scoring model includes image scenario classification information. In this method, scenario information is integrated into a backbone neural network, so that a resulting image aesthetic scoring model is interpretable. In addition, scoring accuracy of the image aesthetic scoring model can be improved by using the preset convolutional structure set.

Image aesthetic processing method and electronic device

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Shenzhen, China

Miao Xie