神经形态布拉耶网络：通过事件驱动的光学触觉感知实现超越单字符的准确且可泛化的布拉耶阅读
Neuromorphic BrailleNet: Accurate and Generalizable Braille Reading Beyond Single Characters through Event-Based Optical Tactile Sensing
AI总结：
本文提出基于事件驱动触觉传感器的神经形态布拉耶网络，通过时空分割与轻量级ResNet分类器实现连续布拉耶识别，实现高精度和多布局泛化，展现低延迟的触觉感知应用潜力。
摘要：
传统机械布拉耶阅读器通常依赖离散的逐字符扫描，限制了阅读速度并破坏了自然流程。基于视觉的替代方案通常需要大量计算，引入延迟，并在现实条件下退化。本文提出了一种高精度、实时的连续布拉耶识别管道，利用开源的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，神经形态触觉模态在连续滑动过程中直接编码动态接触事件，紧密模拟人类手指扫描策略。我们的方法结合时空分割与轻量级ResNet分类器处理稀疏事件流，实现对不同凹陷深度和扫描速度的稳健字符识别。所提出的系统在标准深度下达到接近完美的准确性（≥98%），在多种布拉耶板布局中具有泛化能力，并在快速扫描下保持强性能。在包含日常用语的物理布拉耶板上，系统在词级准确率超过90%，展示了对传统方法所挑战的时间压缩效应的鲁棒性。这些结果将神经形态触觉感知定位为一种可扩展、低延迟的机械布拉耶阅读解决方案，对辅助和机器人应用中的触觉感知具有更广泛的意义。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
