The paper presents an ensemble learning framework for Vietnamese scene text spotting in urban environments. The key highlights are:
The proposed ensemble approach combines the strengths of multiple scene text detection and recognition models to address the complexities of Vietnamese script and urban contexts.
Extensive experiments on the VinText dataset demonstrate that the ensemble framework outperforms individual models, boosting the accuracy by up to 5%. This highlights the efficacy of ensemble learning in advancing scene text spotting in dynamic urban environments.
The authors carefully select and integrate detection methods like DB++, EAST, SAST and recognition models like SPIN, ABINet, SRN to leverage their complementary capabilities. This strategic model combination is crucial for achieving superior performance.
The paper also provides a detailed analysis of individual detection and recognition models, shedding light on their strengths, limitations and the importance of appropriate backbone architectures and fine-tuning on the target dataset.
While the ensemble approach exhibits increased computational complexity, the authors emphasize the need to address challenges like improving spelling accuracy and reducing the overall model complexity in future work.
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arxiv.org
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by Hieu Nguyen,... ב- arxiv.org 04-02-2024
https://arxiv.org/pdf/2404.00852.pdfשאלות מעמיקות