公开(公告)号：US20220301331A1

公开(公告)日：2022-09-22

申请号：US17740585

申请日：2022-05-10

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Sourabh Vasant GOTHE ,  Rakshith S ,  Jayesh Rajkumar Vachhani ,  Yashwant Singh Saini ,  Barath Raj Kandur Raja ,  Himanshu Arora ,  Rishabh Khurana

IPC: G06V30/32

Abstract: Embodiments herein disclose a method and electronic device for predicting multi-modal drawings. The method includes: receiving, by the electronic device, at least one of a text input and strokes of a drawing and determining, by the electronic device, features associated with the text input and features associated with the strokes of the drawing. The method includes classifying, by the electronic device, the features associated with the text input and the features associated with the strokes of the drawing into one of a dominant feature and a non-dominant feature and performing, by the electronic device, early concatenation or late concatenation of the features based on the classification; classifying, by the electronic device, the strokes of the drawing based on the concatenation into a category using a deep neural network (DNN) model; and predicting, by the electronic device, primary drawings corresponding to the category.

公开(公告)号：US11776289B2

公开(公告)日：2023-10-03

申请号：US17740585

申请日：2022-05-10

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Sourabh Vasant Gothe ,  Rakshith S ,  Jayesh Rajkumar Vachhani ,  Yashwant Singh Saini ,  Barath Raj Kandur Raja ,  Himanshu Arora ,  Rishabh Khurana

IPC: G06V10/44 ,  G06V30/32

CPC classification number: G06V30/347 ,  G06V30/36

Abstract: Embodiments herein disclose a method and electronic device for predicting multi-modal drawings. The method includes: receiving, by the electronic device, at least one of a text input and strokes of a drawing and determining, by the electronic device, features associated with the text input and features associated with the strokes of the drawing. The method includes classifying, by the electronic device, the features associated with the text input and the features associated with the strokes of the drawing into one of a dominant feature and a non-dominant feature and performing, by the electronic device, early concatenation or late concatenation of the features based on the classification; classifying, by the electronic device, the strokes of the drawing based on the concatenation into a category using a deep neural network (DNN) model; and predicting, by the electronic device, primary drawings corresponding to the category.