{"type":"ScholarlyArticle","title":"Towards Accurate Video Text Spotting with Text-wise Semantic Reasoning","authors":[{"name":"Xinyan Zu","slug":"xinyan-zu","given":"Xinyan","family":"Zu","affiliation":"Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science , Fudan University"},{"name":"Haiyang Yu","slug":"haiyang-yu","given":"Haiyang","family":"Yu","affiliation":"Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science , Fudan University"},{"name":"Bin Li","slug":"bin-li","given":"Bin","family":"Li","affiliation":"Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science , Fudan University"},{"name":"Xiangyang Xue","slug":"xiangyang-xue","given":"Xiangyang","family":"Xue","affiliation":"Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science , Fudan University"}],"abstract":"Video text spotting (VTS) aims at extracting texts from videos, where text detection, tracking and recognition are conducted simultaneously. There have been some works that can tackle VTS; however, they may ignore the underlying semantic relationships among texts within a frame. We observe that the texts within a frame usually share similar semantics, which suggests that, if one text is predicted incorrectly by a text recognizer, it still has a chance to be corrected via semantic reasoning. In this paper, we propose an accurate video text spotter, VLSpotter, that reads texts visually, linguistically, and semantically. For ‘visually’, we propose a plug-and-play text-focused super-resolution module to alleviate motion blur and enhance video quality. For ‘linguistically’, a language model is employed to capture intra-text context to mitigate wrongly spelled text predictions. For ‘semantically’, we propose a text-wise semantic reasoning module to model inter-text semantic relationships and reason for better results. The experimental results on multiple VTS benchmarks demonstrate that the proposed VLSpotter outperforms the existing state-of-the-art methods in end-to-end video text spotting.","doi":"10.24963/ijcai.2023/206","url":"https://ijcai.org/proceedings/2023/206","pdf":"https://ijcai.org/proceedings/2023/0206.pdf","markdown":"https://ijcai.org/proceedings/2023/206.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/206","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":206,"pages":{"first":1858,"last":1866},"keywords":["Computer Vision: CV: Video analysis and understanding","Computer Vision: CV: Vision and language"],"videos":[],"references":[{"position":1,"raw":"[Bhunia et al., 2021] Ayan Kumar Bhunia, Aneeshan Sain, Amandeep Kumar, Shuvozit Ghose, Pinaki Nath Chowd- hury, and Yi-Zhe Song. 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