Tactile sensing is essential for robotic manipulation, prosthetics and assistive technologies, yet neuromorphic tactile datasets remain limited compared to their visual counterparts. We introduce STEMNIST, a large-scale neuromorphic tactile dataset extending ST-MNIST from 10 digits to 35 alphanumeric classes (uppercase letters A--Z and digits 1--9), providing a challenging benchmark for event-based haptic recognition. The dataset comprises 7,700 samples collected from 34 participants using a custom \(16\times 16\) tactile sensor array operating at 120 Hz, encoded as 1,005,592 spike events through adaptive temporal differentiation. Following EMNIST's visual character recognition protocol, STEMNIST addresses the critical gap between simplified digit classification and real-world tactile interaction scenarios requiring alphanumeric discrimination. Baseline experiments using conventional CNNs (90.91% test accuracy) and spiking neural networks (89.16%) establish performance benchmarks. The dataset's event-based format, unrestricted spatial variability and rich temporal structure makes it suitable for testing neuromorphic hardware and bio-inspired learning algorithms. STEMNIST enables reproducible evaluation of tactile recognition systems and provides a foundation for advancing energy-efficient neuromorphic perception in robotics, biomedical engineering and human-machine interfaces. The dataset, documentation and codes are publicly available to accelerate research in neuromorphic tactile computing.

翻译：触觉感知对于机器人操作、假肢和辅助技术至关重要，然而与视觉数据集相比，神经形态触觉数据集仍然有限。我们引入了STEMNIST，这是一个大规模神经形态触觉数据集，它将ST-MNIST从10个数字类别扩展到35个字母数字类别（大写字母A–Z和数字1–9），为基于事件的触觉识别提供了一个具有挑战性的基准。该数据集包含从34名参与者收集的7,700个样本，使用一个以120 Hz频率运行的自定义 \(16\times 16\) 触觉传感器阵列，并通过自适应时间微分编码为1,005,592个脉冲事件。遵循EMNIST的视觉字符识别协议，STEMNIST解决了简化的数字分类与需要字母数字识别的真实世界触觉交互场景之间的关键差距。使用传统CNN（测试准确率90.91%）和脉冲神经网络（89.16%）的基线实验建立了性能基准。该数据集基于事件的格式、不受限制的空间变异性和丰富的时间结构，使其适用于测试神经形态硬件和仿生学习算法。STEMNIST支持触觉识别系统的可重复评估，并为推进机器人学、生物医学工程和人机界面中高能效的神经形态感知奠定了基础。数据集、文档和代码均已公开，以加速神经形态触觉计算的研究。