CVPR 2023 – Towards Unified Scene Text Spotting based on Sequence Generation

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.