Conventional robotic Braille readers typically rely on discrete, character-by-character scanning, limiting reading speed and disrupting natural flow. Vision-based alternatives often require substantial computation, introduce latency, and degrade in real-world conditions. In this work, we present a high accuracy, real-time pipeline for continuous Braille recognition using Evetac, an open-source neuromorphic event-based tactile sensor. Unlike frame-based vision systems, the neuromorphic tactile modality directly encodes dynamic contact events during continuous sliding, closely emulating human finger-scanning strategies. Our approach combines spatiotemporal segmentation with a lightweight ResNet-based classifier to process sparse event streams, enabling robust character recognition across varying indentation depths and scanning speeds. The proposed system achieves near-perfect accuracy (>=98%) at standard depths, generalizes across multiple Braille board layouts, and maintains strong performance under fast scanning. On a physical Braille board containing daily-living vocabulary, the system attains over 90% word-level accuracy, demonstrating robustness to temporal compression effects that challenge conventional methods. These results position neuromorphic tactile sensing as a scalable, low latency solution for robotic Braille reading, with broader implications for tactile perception in assistive and robotic applications.

翻译：传统机器人盲文阅读器通常依赖离散的逐字符扫描方式，限制了阅读速度并破坏了自然阅读流。基于视觉的替代方案往往需要大量计算、引入延迟，且在现实条件下性能下降。本研究提出了一种高精度、实时的连续盲文识别流程，采用开源神经形态事件驱动触觉传感器Evetac。与基于帧的视觉系统不同，神经形态触觉模态直接在连续滑动过程中编码动态接触事件，高度模拟人类手指扫描策略。我们的方法将时空分割与轻量级ResNet分类器相结合，以处理稀疏事件流，从而实现对不同压痕深度和扫描速度的鲁棒字符识别。所提出的系统在标准深度下达到接近完美的准确率（≥98%），可泛化至多种盲文板布局，并在快速扫描下保持强劲性能。在包含日常生活词汇的物理盲文板上，该系统实现了超过90%的词级准确率，展现出对挑战传统方法的时间压缩效应的鲁棒性。这些成果确立了神经形态触觉传感作为机器人盲文阅读可扩展、低延迟解决方案的地位，并对辅助技术与机器人应用中的触觉感知具有更广泛的启示意义。