Opportunity summary
Score4.0PainBenchmarks Burmese handwritten digit recognition models, highlighting CNN and PETNN performance for regional NLP/AI research.
Evidence0 refs | 0 sources | 17% coverage
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ARXIV:2603.18597 · COMPUTER VISION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.18597COMPUTER VISIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEYe Kyaw Thu · Thazin Myint Oo · Thepchai Supnithi · arXiv
Benchmarks Burmese handwritten digit recognition models, highlighting CNN and PETNN performance for regional NLP/AI research.
Opportunity summary
Pain Benchmarks Burmese handwritten digit recognition models, highlighting CNN and PETNN performance for regional NLP/AI research.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Benchmarks Burmese handwritten digit recognition models, highlighting CNN and PETNN performance for regional NLP/AI research. We evaluate eleven architectures spanning classical deep learning models (Multi-Layer Perceptron, Convolutional Neural Network, Long Short-Term Memory, Gated Recurrent…
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…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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…
Computer Vision moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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Score4.0Analysis summary
Benchmarks Burmese handwritten digit recognition models, highlighting CNN and PETNN performance for regional NLP/AI research.
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Paper Pack
10.48550/arXiv.2603.18597Benchmarks Burmese handwritten digit recognition models, highlighting CNN and PETNN performance for regional NLP/AI research.
Abstract
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.
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PROBLEM
Benchmarks Burmese handwritten digit recognition models, highlighting CNN and PETNN performance for regional NLP/AI research. We evaluate eleven architectures spanning classical deep learning models (Multi-Layer Perceptron, Convolutional Neural Network, Long Short-Term Memory, G...
METHOD
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 Neu...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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). Code ava...
WHY NOW
Computer Vision moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Benchmarks Burmese handwritten digit recognition models, highlighting CNN and PETNN performance for regional 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).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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). Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Computer Vision moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Benchmarks Burmese handwritten digit recognition models, highlighting CNN and PETNN performance for regional NLP/AI research.
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