US 12,333,836 B2
Font detection method and system using artificial intelligence-trained neural network
Hyuk Lee, Seoul (KR); Hyun Jin Yun, Seoul (KR); Dong Hyuk Park, Seoul (KR); Il Guen Seo, Seoul (KR); and Seung Hyun Kim, Seoul (KR)
Assigned to EEUM, INC, Seoul (KR)
Filed by EEUM, Inc, Seoul (KR)
Filed on Jun. 6, 2022, as Appl. No. 17/833,010.
Claims priority of application No. 10-2021-0074288 (KR), filed on Jun. 8, 2021; and application No. 10-2022-0001799 (KR), filed on Jan. 5, 2022.
Prior Publication US 2022/0392241 A1, Dec. 8, 2022
Int. Cl. G06V 30/18 (2022.01); G06V 10/82 (2022.01); G06V 30/14 (2022.01); G06V 30/166 (2022.01); G06V 30/244 (2022.01); G06V 30/28 (2022.01)
CPC G06V 30/18162 (2022.01) [G06V 10/82 (2022.01); G06V 30/1444 (2022.01); G06V 30/166 (2022.01); G06V 30/245 (2022.01); G06V 30/287 (2022.01)] 12 Claims
OG exemplary drawing
 
1. A font detection method using a neural network, the font detection method comprising:
receiving a target text image including a text;
resizing a horizontal or vertical size to a reference input size according to an aspect ratio of the input target text image; and
inputting the resized target text image into a trained neural network and outputting a font of the text included in the text image,
wherein the neural network is trained with a unit image extracted as a unit region of the reference input size from a training image generated by synthesizing a background with the text, and
wherein the neural network comprises a convolution layer that extracts features through a convolution operation on the input target text image, a pooling layer that extracts a value representing a feature for each channel with respect to an extracted feature map, and a fully connected layer that outputs a probability for each category through an output of the pooling layer.