German Sign Language (DGS) Alphabet

### Context
This dataset was created to train a neural network for real time sign detection, which would be used as automated feedback for a learning application.
The dataset ist based on the normalized hand landmark vectors provided by [mediapipe's handpose](https://google.github.io/mediapipe/solutions/hands.html) in order to make the trained NN invariant to lighting situations or skin colors, which could not be represented in a diverse enough fashion in the dataset.

The dataset is therefore designed to train a NN which categorizes the `MULTI_HAND_LANDMARK ` output of the handpose solution.

### Content

The dataset contains 64 columns with the first column being the sample's label. All static signs (meaning signs not involving movement) of the German Sign language alphabet are represented as 24 classes ('a'-'y', excluding 'j').

All other columns represent the 21 linearized, three-dimensional hand landmarks provided by handpose in their normalized ([0.0, 1.0]) state.

In total the dataset contains ca. 7300 samples with at least 250 samples per class, recorded by 7 different non-native signers.

The dataset is purely made up of recorded samples and does not make use of data augmentation.

### Acknowledgements

This dataset was inspired by the desire to create a German version of the[ Sign Language MNIST dataset](https://www.kaggle.com/datamunge/sign-language-mnist) with a stronger focus on practical applicability.

### Inspiration

Our team is interested in providing a foundation for all kinds of practical applications involving sign language recognition. As with our own work, we appreciate a focus on applications challenging non-signers to engage with sign language in a way that promotes inclusion.

### Ethical considerations

We are aware of the ethical implications of such a dataset and encourage developers to seriously consider research on the ethics of machine learning and sign language to avoid harmful outcomes of well intended projects. For more information on this topic we recommend *Bragg, D., Caselli, N., Hochgesang, J. A., Huenerfauth, M., Katz-Hernandez, L., Koller, O., Kushalnagar, R., Vogler, C., & Ladner, R. E. (2021). The FATE Landscape of Sign Language AI Datasets: An Interdisciplinary Perspective. In ACM Transactions on Accessible Computing (14th ed., Vol. 2, pp. 1-45). Association for Computing Machinery. 10.1145/3436996* as a starting point.

{'Context': '本数据集旨在训练实时手势识别的神经网络，该神经网络将被用于作为学习应用的自动反馈。该数据集基于[mediapipe的handpose](https://google.github.io/mediapipe/solutions/hands.html)提供的标准化手部关键点向量，以便训练的神经网络对光照条件或肤色变化不敏感，这些因素在数据集中无法以足够多样化的形式展现。', 'Content': "因此，该数据集旨在训练一个神经网络，该神经网络对handpose解决方案输出的`MULTI_HAND_LANDMARK`进行分类。数据集包含64列，第一列是样本的标签。所有静态手势（即不涉及运动的手势）的德语手语字母表均表示为24个类别（'a'-'y'，不包括'j'）。其余列代表handpose提供的21个线性化、三维手部关键点，它们在归一化状态（[0.0, 1.0]）下表示。总计，该数据集包含约7300个样本，每个类别至少有250个样本，由7位非母语的手语者录制。该数据集纯粹由记录的样本组成，不使用数据增强技术。", 'Acknowledgements': '本数据集源于创建德语版本的[Sign Language MNIST数据集](https://www.kaggle.com/datamunge/sign-language-mnist)的愿望，更加注重其实用性。', 'Inspiration': '我们的团队致力于为所有涉及手语识别的实用应用提供基础。正如我们的研究工作一样，我们赞赏关注能够促使非手语者以促进包容性方式与手语互动的应用。', 'Ethical considerations': '我们深知此类数据集的伦理影响，并鼓励开发者认真考虑机器学习和手语伦理的研究，以避免良好意图的项目产生有害后果。关于此主题的更多信息，我们建议从*Bragg, D.，Caselli, N.，Hochgesang, J. A.，Huenerfauth, M.，Katz-Hernandez, L.，Koller, O.，Kushalnagar, R.，Vogler, C.，& Ladner, R. E. (2021). The FATE Landscape of Sign Language AI Datasets: An Interdisciplinary Perspective. In ACM Transactions on Accessible Computing (第14版，第2卷，第1-45页). Association for Computing Machinery. 10.1145/3436996*入手。'}