@inproceedings{7c6a202ea6a044429656a66740117f3e,
title = "Braille Recognition Algorithm Based on Robotic Arm Active Tactile Perception",
abstract = "With limited Braille literacy among the visually impaired, assistive technologies that do not depend on prior Braille knowledge are urgently needed. This study presents a Braille recognition framework using PyBullet simulation and a KUKA iiwa robotic arm to actively acquire tactile data from Braille characters. A deep neural network (DNN) is trained on 19-dimensional features combining statistical and temporal force signals. The dataset includes 5,000 custom Braille characters based on a 4{\texttimes}4 dot matrix and 2,000 Chinese Braille characters. The system captures both normal and tangential forces during contact. Experimental results show that recognition accuracy is up to 92 \% across both datasets. The proposed method is low-cost, repeatable, and effective for tactile Braille recognition, offering practical potential for blind assistance and Braille education in resource-constrained environments.",
keywords = "Braille Recognition, Deep Neural Network, Simulation, Tactile Perception",
author = "Mingkun Chen and Xing Lin and Ruikai Liu and Yunjiang Lou",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 26th IEEE China Conference on System Simulation Technology and its Applications, CCSSTA 2025 ; Conference date: 11-07-2025 Through 13-07-2025",
year = "2025",
doi = "10.1109/IEEECONF65522.2025.11137024",
language = "英语",
series = "Proceedings of 2025 IEEE 26th China Conference on System Simulation Technology and its Applications, CCSSTA 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "40--45",
booktitle = "Proceedings of 2025 IEEE 26th China Conference on System Simulation Technology and its Applications, CCSSTA 2025",
address = "美国",
}