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Diffusion augmentation boosts Bangla character recognition accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a confidence-guided diffusion augmentation method to improve the recognition of handwritten Bangla compound characters. This approach uses diffusion models to generate high-quality synthetic character samples, enhanced by Squeeze-and-Excitation blocks and a confidence-based filtering mechanism. When trained on these augmented datasets, several classification architectures, including ResNet50 and Vision Transformers, showed significant performance gains. The best model achieved 89.2% accuracy on the AIBangla dataset, surpassing previous benchmarks and demonstrating the effectiveness of quality-aware augmentation in low-resource script recognition. AI

IMPACT Enhances low-resource script recognition, potentially improving OCR for underserved languages.

RANK_REASON Academic paper detailing a new method for character recognition. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Diffusion augmentation boosts Bangla character recognition accuracy

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Academic paper detailing a new method for character recognition. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Maheen Islam ·

    Confidence-Guided Diffusion Augmentation for Enhanced Bangla Compound Character Recognition

    Recognition of handwritten Bangla compound characters remains a challenging problem due to complex character structures, large intra-class variation, and limited availability of high-quality annotated data. Existing Bangla handwritten character recognition systems often struggle …

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