A benchmark shows lightweight CNNs with transfer learning reach 92-99% accuracy on Arabic handwritten character datasets, but the reported best-model rankings are undermined by best-fold selection and internal contradictions.
Unsuper- vised transfer learning via multi-scale convolutional sparse coding for biomedical applications,
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Leveraging Transfer Learning and Mobile-enabled Convolutional Neural Networks for Improved Arabic Handwritten Character Recognition
A benchmark shows lightweight CNNs with transfer learning reach 92-99% accuracy on Arabic handwritten character datasets, but the reported best-model rankings are undermined by best-fold selection and internal contradictions.