  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.
