Ensemble Learning for Vietnamese Scene Text Spotting in Urban Environments
This work addresses scene text spotting for Vietnamese language applications in urban settings, but it is incremental as it applies existing ensemble techniques to a specific domain.
The paper tackled Vietnamese scene text spotting in urban environments by proposing an ensemble learning framework, achieving a 5% accuracy improvement on the VinText dataset.
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.