Diffusion augmentation boosts Bangla character recognition accuracy

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

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

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

在 arXiv cs.CV 阅读 →

• AIBangla
• Bangla
• DenseNet121
• ResNet50
• Vision Transformer
• VGG16

• 论文
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