Publication
Neuromorphic BrailleNet: Accurate and Generalizable Braille Reading Beyond Single Characters through Event-Based Optical Tactile Sensing
Naqash Afzal; Niklas Funk; Erik Helmut; Jan Peters; Benjamin Ward-Cherrier
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2601.19079, Pages 1-8, arXiv, 2026.
Abstract
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 impli-
cations for tactile perception in assistive and robotic applications.