TY  - STD
AU  - Alloy Das
AU  - Sanket Biswas
AU  - Umapada Pal
AU  - Josep Llados
PY  - 2023//
TI  - Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes
N2  - 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.
UR  - https://arxiv.org/abs/2310.00558
L1  - http://refbase.cvc.uab.es/files/DBP2023.pdf
N1  - DAG
ID  - Alloy Das2023
ER  -