This paper presents a simple yet efficient ensemble learning framework for
Vietnamese scene text spotting. Leveraging the power of ensemble learning,
which combines multiple models to yield more accurate predictions, our approach
aims to significantly enhance the performance of scene text spotting in
challenging urban settings. Through experimental evaluations on the VinText
dataset, our proposed method achieves a significant improvement in accuracy
compared to existing methods with an impressive accuracy of 5%. These results
unequivocally demonstrate the efficacy of ensemble learning in the context of
Vietnamese scene text spotting in urban environments, highlighting its
potential for real world applications, such as text detection and recognition
in urban signage, advertisements, and various text-rich urban scenes.

提出了一个简单而高效的整合学习框架用于越南场景文字定位。通过结合多个模型以提高预测准确性的整合学习方法，旨在显著提升在具有挑战性的城市环境下的场景文字定位性能。通过在 VinText 数据集上的实验评估，我们提出的方法相对于现有方法在准确性方面取得了显著改进，达到了 5% 的令人印象深刻的准确性。这些结果明确证明了在越南城市环境中的场景文字定位中整合学习的效果，并强调了其在真实世界应用方面的潜力，如城市标识、广告和各种富有文字的城市场景中的文本检测和识别。