In this episode we discuss Towards Unified Scene Text Spotting based on Sequence Generation
by Taeho Kil, Seonghyeon Kim, Sukmin Seo, Yoonsik Kim, Daehee Kim. The proposed paper presents a UNIfied scene Text Spotter, called UNITS, to overcome the limitations of auto-regressive models used for end-to-end text spotting. UNITS unifies various detection formats, allowing it to detect text in arbitrary shapes, and applies starting-point prompting to extract more texts beyond the number of instances it was trained on. Experimental results show that UNITS achieves competitive performance compared to state-of-the-art methods and can extract a larger number of texts than it was trained on. Code for the method is provided on GitHub.
CVPR 2023 – Towards Unified Scene Text Spotting based on Sequence Generation
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