BHDD: A Burmese Handwritten Digit Dataset
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Abstract (arXiv)
We introduce the Burmese Handwritten Digit Dataset (BHDD), a collection of 87,561 grayscale images of handwritten Burmese digits in ten classes. Each image is 28x28 pixels, following the MNIST format. The training set has 60,000 samples split evenly across classes; the test set has 27,561 samples with class frequencies as they arose during collection. Over 150 people of different ages and backgrounds contributed samples. We analyze the dataset's class distribution, pixel statistics, and morphological variation, and identify digit pairs that are easily confused due to the round shapes of the Myanmar script. Simple baselines (an MLP, a two-layer CNN, and an improved CNN with batch normalization and augmentation) reach 99.40%, 99.75%, and 99.83% test accuracy respectively. BHDD is available under CC BY-SA 4.0 at https://github.com/baseresearch/BHDD
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References · 5
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2018Deep Learning for Classical Japanese Literaturecited by 415
- 2017Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognitioncited by 13
- 2019Kannada-MNIST: A new handwritten digits dataset for the Kannada languagecited by 13
- 2018NumtaDB - Assembled Bengali Handwritten Digitscited by 8
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