Handwritten Prescription Recognition Through Transfer Learning Models Evaluation Under Multiple Dataset Partitions

1. Before the model evaluation, a duplicate-aware dataset verification and leakage-control framework was developed with the help of exact image hashing and perceptual grouping.

2. Four different train-validation-test partition strategies were systematically evaluated to investigate the effect of data partitioning on model performance.

3. The five transfer-learning architectures including VGG16, DenseNet121, ResNet50, EfficientNet-B0 and MobileNetV3-Large are trained on the same training and evaluation settings.

4. To prevent test-set selection of the final model, a validation-based model-selection strategy was used, with Macro-F1 as the validation measure.

5. To test the stability and robustness of the selected configuration, and to avoid visual-group leakage between folds, five-fold stratified cross-validation is employed.