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：[译] 传统机器人盲文阅读器通常依赖逐字符离散扫描，这限制了阅读速度并破坏了自然阅读的连贯性。基于视觉的替代方案往往需要大量计算，引入延迟，并在实际环境中性能下降。本文提出一种利用Evetac开源神经形态事件触觉传感器实现连续盲文识别的高精度实时处理流水线。与基于帧的视觉系统不同，神经形态触觉模态直接编码连续滑动过程中的动态接触事件，紧密模拟人类手指扫描策略。本方法结合时空分割技术与轻量级基于ResNet的分类器处理稀疏事件流，实现在不同压痕深度和扫描速度下的鲁棒字符识别。所提系统在标准深度下达到近乎完美的准确率（≥98%），可泛化至多种盲文板布局，并在快速扫描下保持优异性能。在包含日常词汇的物理盲文板上，系统实现超过90%的词汇级准确率，展现出对困扰传统方法的时间压缩效应的鲁棒性。这些结果确立了神经形态触觉感知作为可扩展、低延迟的机器人盲文阅读解决方案的地位，并为辅助与机器人应用中的触觉感知提供了更广泛启示。