CNIPA.AI
Back to Search
Dossier

MACHINE-LEARNING MODELS FOR IMAGE PROCESSING

InventionPending
20Claims · 2 independent
§ Ⅰ

Dossier Overview

Inventor

Ashutosh K. SUREKA; Venkata Sesha Kiran Kumar ADIMATYAM; Miriam SILVER; Daniel FUNKEN

IPC Classification

G6T 7/G6V 10/74G6V 20/70

CPC Classification

G6T7/2G6V10/761G6V20/70G6T2207/30168G6V2201/7

Presented herein are systems and methods for the employment of machine learning models for image processing. A method may include a capture of a video feed including image data of a document at a client device. The client device can provide the video feed to another computing device. The method can include, by the client device or the other computing device object recognition for recognizing a type of document and capturing an image exceeding a quality threshold of the document amongst the frames within the video feed. The method may further include the execution of other image processing operations on the image data to improve the quality of the image or features extracted therefrom. The method may further include anti-fraud detection or scoring operations to determine an amount of risk associated with the image data.

Original (Chinese)

Presented herein are systems and methods for the employment of machine learning models for image processing. A method may include a capture of a video feed including image data of a document at a client device. The client device can provide the video feed to another computing device. The method can include, by the client device or the other computing device object recognition for recognizing a type of document and capturing an image exceeding a quality threshold of the document amongst the frames within the video feed. The method may further include the execution of other image processing operations on the image data to improve the quality of the image or features extracted therefrom. The method may further include anti-fraud detection or scoring operations to determine an amount of risk associated with the image data.

External Resources