{"type":"ScholarlyArticle","title":"TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition","authors":[{"name":"Tianlun Zheng","slug":"tianlun-zheng","given":"Tianlun","family":"Zheng","affiliation":"Shanghai Collaborative Innovation Center of Intelligent Visual Computing , School of Computer Science , Fudan University , China"},{"name":"Zhineng Chen","slug":"zhineng-chen","given":"Zhineng","family":"Chen","affiliation":"Shanghai Collaborative Innovation Center of Intelligent Visual Computing , School of Computer Science , Fudan University , China"},{"name":"Jinfeng Bai","slug":"jinfeng-bai","given":"Jinfeng","family":"Bai","affiliation":"Tomorrow Advance Life , China"},{"name":"Hongtao Xie","slug":"hongtao-xie","given":"Hongtao","family":"Xie","affiliation":"University of Science and Technology of China , China"},{"name":"Yu-Gang Jiang","slug":"yu-gang-jiang","given":"Yu-Gang","family":"Jiang","affiliation":"Shanghai Collaborative Innovation Center of Intelligent Visual Computing , School of Computer Science , Fudan University , China"}],"abstract":"Text irregularities pose significant challenges to scene text recognizers. Thin-Plate Spline (TPS)-based rectification is widely regarded as an effective means to deal with them. Currently, the calculation of TPS transformation parameters purely depends on the quality of regressed text borders. It ignores the text content and often leads to unsatisfactory rectified results for severely distorted text. In this work, we introduce TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time. TPS++ formulates the parameter calculation as a joint process of foreground control point regression and content-based attention score estimation, which is computed by a dedicated designed gated-attention block. TPS++ builds a more flexible content-aware rectifier, generating a natural text correction that is easier to read by the subsequent recognizer. Moreover, TPS++ shares the feature backbone with the recognizer in part and implements the rectification at feature-level rather than image-level, incurring only a small overhead in terms of parameters and inference time. Experiments on public benchmarks show that TPS++ consistently improves the recognition and achieves state-of-the-art accuracy. Meanwhile, it generalizes well on different backbones and recognizers. Code is at https://github.com/simplify23/TPS_PP.","doi":"10.24963/ijcai.2023/197","url":"https://ijcai.org/proceedings/2023/197","pdf":"https://ijcai.org/proceedings/2023/0197.pdf","markdown":"https://ijcai.org/proceedings/2023/197.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/197","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":197,"pages":{"first":1777,"last":1785},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Scene analysis and understanding"],"videos":[],"references":[{"position":1,"raw":"[Baek et al., 2019] Jeonghun Baek, Geewook Kim, Jun- yeop Lee, Sungrae Park, Dongyoon Han, Sangdoo Yun, Seong Joon Oh, and Hwalsuk Lee. 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