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This paper introduces a benchmark of eleven different neural network architectures, including classical deep learning models, KAN variants, and PETNNs, on the myMNIST Burmese handwritten digit dataset. The study evaluates the performance of these models using precision, recall, F1-score, and accuracy, finding that CNNs and PETNNs (GELU) achieve the best results, outperforming LSTMs, GRUs, Transformers, and KANs. The benchmark establishes reproducible baselines for myMNIST and highlights the potential of PETNNs for regional script recognition.
CNNs still reign supreme in Burmese handwritten digit recognition, but physics-inspired PETNNs are hot on their heels, outperforming Transformers and KANs.
We present the first systematic benchmark on myMNIST (formerly BHDD), a publicly available Burmese handwritten digit dataset important for Myanmar NLP/AI research. We evaluate eleven architectures spanning classical deep learning models (Multi-Layer Perceptron, Convolutional Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Transformer), recent alternatives (FastKAN, EfficientKAN), an energy-based model (JEM), and physics-inspired PETNN variants (Sigmoid, GELU, SiLU). Using Precision, Recall, F1-Score, and Accuracy as evaluation metrics, our results show that the CNN remains a strong baseline, achieving the best overall scores (F1 = 0.9959, Accuracy = 0.9970). The PETNN (GELU) model closely follows (F1 = 0.9955, Accuracy = 0.9966), outperforming LSTM, GRU, Transformer, and KAN variants. JEM, representing energy-based modeling, performs competitively (F1 = 0.9944, Accuracy = 0.9958). KAN-based models (FastKAN, EfficientKAN) trail the top performers but provide a meaningful alternative baseline (Accuracy ~0.992). These findings (i) establish reproducible baselines for myMNIST across diverse modeling paradigms, (ii) highlight PETNN's strong performance relative to classical and Transformer-based models, and (iii) quantify the gap between energy-inspired PETNNs and a true energy-based model (JEM). We release this benchmark to facilitate future research on Myanmar digit recognition and to encourage broader evaluation of emerging architectures on regional scripts.
