Abstract
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.
Abstract (translated)
本文提出了一种简单的但高效的聚类学习框架,用于越南场景文本检测。利用聚类学习的优势,该方法结合多个模型以产生更准确的预测,旨在显著增强具有挑战性城市环境中的场景文本检测的性能。通过对VinText数据集的实验评估,与现有方法相比,我们提出的方法在准确性方面显著提高,达到令人印象深刻的5%的准确度。这些结果无条件地证明了在越南城市环境中使用聚类学习提高场景文本检测效果的可能性,并突出了其在城市标志、广告和各种充满文本的城市场景等现实应用中的潜力。
URL
https://arxiv.org/abs/2404.00852
