Paper Overview
- Field: Computer Vision (CV)
- Authors: Md. Sultan Al Rayhan, Maheen Islam
- Published: 2025-05-09
- arXiv: 2505.07237
- Combines class-conditional diffusion modeling with classifier guidance to synthesize high-quality handwritten compound character samples.
- Introduces Squeeze-and-Excitation enhanced residual blocks in the diffusion model's U-Net backbone to further improve generation quality.
- Applies a confidence-based filtering mechanism in which a pretrained classifier acts as a quality gate, retaining only highly class-consistent synthetic samples.
- Fuses the filtered synthetic images with original training data to retrain multiple classification architectures.
Summary
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 to generalize across diverse writing styles, particularly for compound characters containing intricate ligatures and diacritical variations.
The authors propose a confidence-guided diffusion augmentation framework for low-resolution Bangla compound character recognition:
Results
Experiments on the AIBangla compound character dataset show consistent performance improvements across ResNet50, DenseNet121, VGG16, and Vision Transformer architectures. The best-performing model achieves 89.2% classification accuracy, substantially surpassing previously published AIBangla benchmarks.
Conclusion
Quality-aware diffusion augmentation can effectively improve handwritten character recognition performance in low-resource script domains.
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