| Sumario: | Abstract\nIMPLICATIONS TO REHABILITATIONObjective: The objective of OptiBraille is to develop a low-cost, standalone assistive Braille character recognition system designed and built on a Raspberry Pi 4, enabling visually impaired users to access written Braille in real time. The system aims to provide real-time offline conversion of Braille to text without requiring a network connection or a high-cost device. It also aims to integrate edge AI inference and text-to-speech feedback into a portable assistive device suitable for resource-constrained environments.Methods: OptiBraille captures Braille images using a Pi Camera V2 and preprocesses them through RGB normalisation. A MobileNetV2-based convolutional neural network (CNN) is used to recognise characters A–Z. The model is trained using three-stage progressive fine-tuning and optimised through post-training quantisation to TensorFlow Lite format. A cross-domain generalisation evaluation framework is employed using multi-source training data comprising synthetic, scanned, and real-photograph datasets, with an Angelina-only validation strategy to optimise real-world deployment performance. The V3 model is deployed on Raspberry Pi 4 for real-time, fully offline inference and integrated with text-to-speech feedback. The system achieves 92.55% real-world accuracy on photographically diverse Braille book images, with a quantified cross-domain generalisation gap of 7.45% relative to synthetic test accuracy and a compressed model size of 2.71 MB. The prototype uses a Raspberry Pi 4 and Pi Camera V2.Impact: OptiBraille provides an affordable and portable assistive technology solution that enables visually impaired users to convert Braille into text and receive real-time speech feedback without internet connectivity. Its low-cost hardware, lightweight deep learning model, and offline operation make it suitable for resource-constrained environments. The cross-domain generalisation evaluation demonstrates the system’s ability to address the performance challenges associated with real-world Braille images. The architecture can also be ported to custom embedded boards with minimal hardware, supporting wider deployment. Future extensions include multi-line Braille recognition, Grade-2 Braille support, and smartphone integration.Supports independent learning and access to written information for visually impaired individuals through real-time audio feedback.Reduces dependence on caregivers, educators, or sighted assistance for Braille interpretation.Assists rehabilitation professionals, teachers, and caregivers who are unfamiliar with Braille by converting Braille content into speech output. Promotes inclusive communication between visually impaired users and the broader community.Supports independent learning and access to written information for visually impaired individuals through real-time audio feedback.Reduces dependence on caregivers, educators, or sighted assistance for Braille interpretation.Assists rehabilitation professionals, teachers, and caregivers who are unfamiliar with Braille by converting Braille content into speech output. Promotes inclusive communication between visually impaired users and the broader community.
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