构建首个缅甸手写数字数据集，助力本地AI研究
BHDD: A Burmese Handwritten Digit Dataset
- 收集87,561张28x28像素缅甸手写数字图像，按MNIST格式组织
- 简单模型最高达99.83%准确率，但部分圆体数字易混淆
- 适合缅甸文字识别、低资源语言场景的机器学习研究者
我们提出缅甸手写数字数据集（BHDD），包含87,561张灰度图像，涵盖10个类别，每张图像为28x28像素，符合MNIST格式。训练集含60,000样本，各类别均匀分布；测试集含27,561样本，类别分布反映实际采集情况。超过150名不同年龄和背景的人参与数据采集。我们分析了数据集的类别分布、像素统计及形态变化，发现因缅甸文字圆体特征导致部分数字易混淆。采用MLP、两层CNN及引入批量归一化与增强的改进CNN作为基线模型，测试准确率分别为99.40%、99.75%和99.83%。BHDD可在https://github.com/baseresearch/BHDD 下载，许可协议为CC BY-SA 4.0。
原文摘要 · Abstract (English)
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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