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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.19079 |
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| _version_ | 1866914490698694656 |
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| author | Afzal, Naqash Funk, Niklas Helmut, Erik Peters, Jan Ward-Cherrier, Benjamin |
| author_facet | Afzal, Naqash Funk, Niklas Helmut, Erik Peters, Jan Ward-Cherrier, Benjamin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19079 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neuromorphic BrailleNet: Accurate and Generalizable Braille Reading Beyond Single Characters through Event-Based Optical Tactile Sensing Afzal, Naqash Funk, Niklas Helmut, Erik Peters, Jan Ward-Cherrier, Benjamin Robotics 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. |
| title | Neuromorphic BrailleNet: Accurate and Generalizable Braille Reading Beyond Single Characters through Event-Based Optical Tactile Sensing |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.19079 |