Visually Impaired Persons (VIPs) face daily challenges in accessing written information, which is crucial for knowledge and education. Braille, a system of raised dots representing letters and numbers, helps VIPs read and write. However, only about 10% of legally blind individuals are proficient in Braille due to the bulkiness and cost of Braille materials and a shortage of qualified teachers. This issue significantly impacts children and adolescents, as early Braille literacy is vital for their future education, employment, and overall life outcomes.
Our goal was to create a cost-effective, portable device that can translate written text into Braille using Optical Character Recognition (OCR) for the purposes of expanding the deficit of Braille literacy. Our device features a Refreshable Electromechanical Braille display powered by solenoids and cam actuators. This actuator includes an eccentric cam with an embedded rare-earth magnet that rotates to two stable positions via the solenoid electromagnet changing its polarity, raising or lowering a Braille dot. The final device's frame is designed in SolidWorks and Onshape CAD, and 3D printed. Electrical components are constructed on a PCB board with an Arduino circuit. Text-to-speech (TTS) will be implemented using a speech application program interface (SAPI), converting text to audio. Batteries ensure the device remains portable and powered.
Using an SLA 3D printer, various scaled models of Braille cells (1x, 2x, 5x, and 10x) were printed for observation and experimentation. The 10x model was the most intact and used for functionality testing. A solenoid winding tool was also printed to facilitate solenoid creation, essential for the braille display. The electromechanical component involves solenoids generating an electromagnetic field to rotate the cam, raising the Braille pin. The PCB board assembly involved small, precise soldering. The PCB board was regulated by an Arduino Nano, sending instructions to each smaller PCB holding a braille cell. For OCR and text conversion, a Raspberry Pi with a camera uses the Pytesseract library for OCR and pybraille for Text-to-Braille conversion, displaying results on a web server.