公开(公告)号：US20190147304A1

公开(公告)日：2019-05-16

申请号：US15812548

申请日：2017-11-14

Applicant: Adobe Inc.

Inventor： Yang Liu ,  Zhaowen Wang ,  I-Ming Pao ,  Hailin Jin

IPC: G06K9/68 ,  G06K9/62 ,  G06N3/08 ,  G06N5/04

CPC classification number: G06K9/6828 ,  G06K9/6227 ,  G06K9/6257 ,  G06K9/6262 ,  G06K9/6277 ,  G06K9/628 ,  G06N3/0454 ,  G06N3/08 ,  G06N3/084 ,  G06N5/046

Abstract: The present disclosure relates to a font recognition system that employs a multi-task learning framework and training to improve font classification and remove negative side effects caused by intra-class variances of glyph content. For example, in one or more embodiments, the font recognition system trains a hybrid font recognition neural network that includes two or more font recognition neural networks and a weight prediction neural network. The hybrid font recognition neural network determines and generates classification weights based on which font recognition neural network within the hybrid font recognition neural network is best suited to classify the font in an input text image. By employing a hybrid trained font classification neural network, the font recognition system can improve overall font recognition as well as remove the negative side effects from diverse glyph content.

公开(公告)号：US20190138860A1

公开(公告)日：2019-05-09

申请号：US15807028

申请日：2017-11-08

Applicant: Adobe Inc.

Inventor： Yang Liu ,  Zhaowen Wang ,  Hailin Jin

IPC: G06K9/68 ,  G06K9/62

CPC classification number: G06K9/6828 ,  G06K9/00422 ,  G06K9/6256 ,  G06K9/6273 ,  G06K9/6277 ,  G06K9/66 ,  G06K2209/011 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/084

Abstract: The present disclosure relates to a font recognition system that employs a multi-task learning framework and adversarial training to improve font classification and remove negative side effects caused by intra-class variances of glyph content. For example, in one or more embodiments, the font recognition system adversarial trains a font recognition neural network by minimizing font classification loss while at the same time maximizing glyph classification loss. By employing an adversarially trained font classification neural network, the font recognition system can improve overall font recognition by removing the negative side effects from diverse glyph content.

公开(公告)号：US20190108203A1

公开(公告)日：2019-04-11

申请号：US15729855

申请日：2017-10-11

Applicant: Adobe Inc.

Inventor： Zhaowen Wang ,  Hailin Jin ,  Aaron Phillip Hertzmann ,  Shuhui Jiang

IPC: G06F17/21 ,  G06N3/04 ,  G06K9/62 ,  G06F9/44

Abstract: The present disclosure relates to an asymmetric font pairing system that efficiently pairs digital fonts. For example, in one or more embodiments, the asymmetric font pairing system automatically identifies and provides users with visually aesthetic font pairs for use in different sections of an electronic document. In particular, the asymmetric font pairing system learns visually aesthetic font pairs using joint symmetric and asymmetric compatibility metric learning. In addition, the asymmetric font pairing system provides compact compatibility spaces (e.g., a symmetric compatibility space and an asymmetric compatibility space) to computing devices (e.g., client devices and server devices), which enable the computing devices to quickly and efficiently provide font pairs to users.