Real-Time Braille Letter Detection System Using YOLOv8
DOI:
https://doi.org/10.30871/jaic.v9i4.10060Keywords:
Braille, Datasets, Roboflow, YOLOv8Abstract
The purpose of this research is to create a system capable of detecting and recognizing Braille letters in real-time using the YOLOv8 algorithm for object detection, integrated with image processing technology and a user interface based on Tkinter. This system is developed to support visually impaired individuals in reading Braille text through the use of a webcam that captures and identifies Braille letters from images. The identification process is carried out by comparing the obtained images with a precompiled database of Braille letters. This research utilizes a dataset consisting of images of Braille code from letters A to Z, collected through public and private methods, with a total of 6013 images that comprehensively represent Braille letters. The model training is done using YOLOv8 to recognize Braille letter objects, with model performance evaluation using the Mean Average Precision (mAP) metric.The results of the tests show a very satisfactory model performance, with a mAP50 score of 0.98 and a mAP50-95 score of 0.789, as well as a high accuracy rate for almost all Braille letters tested. In addition, the system is equipped with a Tkinter-based graphical user interface (GUI) that allows users to operate the Braille letter detection process interactively and easily. This research proves that the YOLOv8-based object detection approach has significant potential for Braille letter recognition applications, which is expected to enhance accessibility and the independence of visually impaired individuals in reading text effectively.
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Copyright (c) 2025 Reyshano Adhyarta Himawan, Eko Hari Rachmawanto, Christy Atika Sari
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