{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/henet-forcing-a-network-to-think-more-for","title":"HENet: Forcing a Network to Think More for Font Recognition","arxiv_id":"2110.10872","date":"2021-10-21","proceeding":null,"authors":["Jingchao Chen","Shiyi Mu","Shugong Xu","Youdong Ding"],"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","url_abs":"https://arxiv.org/abs/2110.10872v1","url_pdf":"https://arxiv.org/pdf/2110.10872v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"henet-forcing-a-network-to-think-more-for","repo_url":"https://github.com/PhamQuocHuy1101/transfer-learning-template/blob/master/network/henet.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"font-recognition","task_name":"Font Recognition"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/font-recognition-on-adobevfr-real","task":"Font Recognition","dataset":"AdobeVFR real","model":"HENet (ResNet18+HE Block)","rank_in_archive_order":2,"of":2,"metrics":{"Top 1 Accuracy":"47.41","Top 5 Accuracy":"65.11"},"uses_additional_data":false},{"leaderboard":"/sota/font-recognition-on-adobevfr-syn","task":"Font Recognition","dataset":"AdobeVFR syn","model":"HENet (ResNet18+HE Block)","rank_in_archive_order":2,"of":5,"metrics":{"Top 1 Accuracy":"98.23","Top 5 Accuracy":"99.98"},"uses_additional_data":false},{"leaderboard":"/sota/font-recognition-on-explor-all","task":"Font Recognition","dataset":"Explor_all","model":"HENet","rank_in_archive_order":1,"of":1,"metrics":{"Top 1 Accuracy":"86.31","Top 5 Accuracy":"98.48"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}