@inproceedings{033211c9c7b94567a8eff2e6867006b2,
title = "HENet: Forcing a Network to Think More for Font Recognition",
abstract = "Although lots of progress were made in Text Recognition /OCR in recent years, the task of font recognition is remaining challenging. The main challenge lies in the subtle difference between these similar fonts, which is hard to distinguish. This paper proposes a novel font recognizer with a pluggable module solving the font recognition task. The pluggable module hides the most discriminative accessible features and forces the network to consider other complicated features to solve the hard examples of similar fonts, called HE Block. Compared with the available public font recognition systems, our proposed method does not require any interactions at the inference stage. Extensive experiments demonstrate that HENet achieves encouraging performance, including on character-level dataset Explor all and word-level dataset AdobeVFR.",
keywords = "font recognition, neural networks, pluggable",
author = "Jingchao Chen and Shiyi Mu and Shugong Xu and Youdong Ding",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 3rd International Conference on Advanced Information Science and System, AISS 2021 ; Conference date: 26-11-2021 Through 28-11-2021",
year = "2021",
month = nov,
day = "26",
doi = "10.1145/3503047.3503055",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "2021 3rd International Conference on Advanced Information Science and System, AISS 2021 - Conference Proceedings",
}