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Author (up) Alloy Das; Sanket Biswas; Umapada Pal; Josep Llados edit   pdf
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  Title Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes Type Conference Article
  Year 2024 Publication IEEE International Conference on Robotics and Automation in PACIFICO Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system. However, existing state-of-the-art methods employ pretraining and fine-tuning strategies on natural scene datasets, which do not exploit the feature interaction across other complex domains. In this work, we explore and investigate the problem of domain-agnostic scene text spotting, i.e., training a model on multi-domain source data such that it can directly generalize to target domains rather than being specialized for a specific domain or scenario. In this regard, we present the community a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes to establish an important case study. Moreover, we also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter which achieves comparable or superior performance over existing text spotting architectures for both regular and arbitrary-shaped scene text spotting benchmarks in terms of both accuracy and model efficiency. The dataset, code and pre-trained models will be released upon acceptance.  
  Address Yokohama; Japan; May 2024  
  Corporate Author Thesis  
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  Area Expedition Conference ICRA  
  Notes DAG Approved no  
  Call Number Admin @ si @ DBP2024 Serial 3979  
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