BdSL-MNIST

the existing datasets offer very limited variety in terms of background, light contrast, skin tone, capture angle, number of subjects, and image scaling. As the development of an efficient learning model to deal with real-world scenarios requires a large variety of data, we endeavored to create a large dataset of one-handed BdSL alphabet. We gathered 35,149 images of 37 one-handed BdSL signs combining different previously introduced datasets and labeled the images into 37 classes where each class possesses 950–1000 images. The images have over 150 types of background and a broad range of light contrast, hand size, image scale, and skin tone of hand. The images are captured from more than 350 subjects and various angles. BdSL-MNIST is a newly developed dataset consisting of 64x64 pixel images. BdSL-MNIST provides a valuable resource for researchers and developers working on sign language recognition systems for Bengali-speaking individuals.现有数据集在背景环境、光照对比度、肤色、拍摄角度、参与拍摄人数以及图像缩放比例等维度上的多样性均极为有限。鉴于适配真实场景的高效学习模型研发需要大量多样化的数据支撑，我们倾力构建了一款大型单手孟加拉语手语（BdSL）字母数据集。我们整合此前已发布的多组数据集，采集了涵盖37种单手孟加拉语手语手势的35149张图像，并将其标注为37个类别，每个类别包含950至1000张图像。该数据集的图像涵盖超过150种不同背景，且光照对比度、手部尺寸、图像缩放比例以及手部肤色的分布范围均较为广泛。图像的拍摄参与者超过350人，拍摄角度亦覆盖多种维度。BdSL-MNIST是一款全新构建的数据集，其图像分辨率统一为64×64像素。BdSL-MNIST可为从事孟加拉语使用者手语识别系统研发的研究人员与开发者提供极具价值的科研资源。