Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes

• URL: http://arxiv.org/abs/2310.00558v3
• Date: Sat, 17 Feb 2024 14:10:25 GMT
• Title: Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes
• Authors: Alloy Das, Sanket Biswas, Umapada Pal and Josep Llad\'os
• Abstract summary: We present a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes. We also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter. The dataset, code and pre-trained models will be released upon acceptance.
• Score: 11.478236584340255
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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