{"type":"ScholarlyArticle","title":"RZCR: Zero-shot Character Recognition via Radical-based Reasoning","authors":[{"name":"Xiaolei Diao","slug":"xiaolei-diao","given":"Xiaolei","family":"Diao","affiliation":"College of Computer Science and Technology , Jilin University; DISI , University of Trento"},{"name":"Daqian Shi","slug":"daqian-shi","given":"Daqian","family":"Shi","affiliation":"DISI , University of Trento"},{"name":"Hao Tang","slug":"hao-tang","given":"Hao","family":"Tang","affiliation":"CVL , ETH Zurich"},{"name":"Qiang Shen","slug":"qiang-shen","given":"Qiang","family":"Shen","affiliation":"College of Computer Science and Technology , Jilin University"},{"name":"Yanzeng Li","slug":"yanzeng-li","given":"Yanzeng","family":"Li","affiliation":"College of Software Engineering , Jilin University"},{"name":"Lei Wu","slug":"lei-wu","given":"Lei","family":"Wu","affiliation":"College of Software Engineering , Jilin University"},{"name":"Hao Xu","slug":"hao-xu","given":"Hao","family":"Xu","affiliation":"College of Computer Science and Technology , Jilin University"}],"abstract":"The long-tail effect is a common issue that limits the performance of deep learning models on real-world datasets. Character image datasets are also affected by such unbalanced data distribution due to differences in character usage frequency. Thus, current character recognition methods are limited when applied in the real world, especially for the categories in the tail that lack training samples, e.g., uncommon characters. In this paper, we propose a zero-shot character recognition framework via radical-based reasoning, called RZCR, to improve the recognition performance of few-sample character categories in the tail. Specifically, we exploit radicals, the graphical units of characters, by decomposing and reconstructing characters according to orthography. RZCR consists of a visual semantic fusion-based radical information extractor (RIE) and a knowledge graph character reasoner (KGR). RIE aims to recognize candidate radicals and their possible structural relations from character images in parallel. The results are then fed into KGR to recognize the target character by reasoning with a knowledge graph. We validate our method on multiple datasets, and RZCR shows promising experimental results, especially on few-sample character datasets.","doi":"10.24963/ijcai.2023/73","url":"https://ijcai.org/proceedings/2023/73","pdf":"https://ijcai.org/proceedings/2023/0073.pdf","markdown":"https://ijcai.org/proceedings/2023/73.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/73","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":73,"pages":{"first":654,"last":662},"keywords":["Computer Vision: CV: Vision and language","Multidisciplinary Topics and Applications: MDA: Humanities"],"videos":[],"references":[{"position":1,"raw":"[Akata et al., 2015] Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid. 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