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This study presents a simplified MSER pruning method to improve text detection in natural images by reducing non-text and duplicate candidates while maintaining recall. The proposed approach involves classifying text candidates into letter and word groups, and it successfully reduces letter candidates by 60% without compromising recall, outperforming previous MSER-based methods. The research highlights the challenges of text detection due to varying conditions and proposes a structured method to enhance the

Author

Ednawati Rainarli

Language

EN