Wearable Braille Reader Using Deep Learning for Real-Time Speech
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Solution Overview
Problem
Existing Braille recognition technologies fail to effectively convert printed Braille dots to speech in real-time, often requiring visual input or lacking tactile feedback, which hinders the learning experience for visually impaired individuals.
Innovation Solution
A wearable device with a 3D ring case and digital camera that captures Braille images, processed by a microprocessor using a deep learning-based convolutional neural network to convert Braille patterns into audio, allowing real-time recognition and speech output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a contact type image sensor is used to capture Braille images, then the Braille pattern can be recognized, but the sensor comes in contact with the Braille pattern instead of the user's finger, preventing tactile feedback for learning
Solution Approach 1:
The patent introduces a transparent film as an intermediary between the contact image sensor and the Braille pattern. The user's finger touches the transparent film while the sensor captures the Braille pattern through the film, allowing tactile feedback to reach the user while maintaining accurate image capture. This mediator resolves the conflict between measurement precision and ease of operation.
2Measurement precision
If a camera-based system is used to capture Braille images, then recognition can be achieved, but a blind person cannot orient the camera without vision
Solution Approach 1:
The patent makes the camera system self-orienting by attaching it to the user's finger. The camera automatically positions itself over the Braille pattern as the user moves their finger across the text, eliminating the need for manual orientation. This self-service approach resolves the contradiction between accurate image capture and ease of operation for blind users.
3Measurement precision
If semantic segmentation models are used to process Braille images, then the Braille pattern can be identified, but the output is an image which is not readable by a blind person
Solution Approach 1:
The patent replaces the visual output mechanism (image display) with an auditory output mechanism (speech synthesis). The processed Braille pattern information is converted from visual form to audible speech through text-to-speech conversion, allowing blind users to access the information. This substitution resolves the contradiction between accurate pattern identification and information accessibility.
4Productivity
If a deep learning-based recognition system is implemented, then real-time Braille recognition can be achieved, but the system complexity increases
Solution Approach 1:
The patent segments the deep learning system into modular components: image capture module, pre-processing module, neural network recognition module, and speech output module. Each module performs a specific function and can be independently optimized or replaced. This segmentation manages system complexity while maintaining real-time recognition capability.
Data Source
AI summary
A device, method, and system for converting printed Braille dots to speech. A Braille image of the printed Braille dots is captured by a digital camera mounted on a 3D ring case. Data processing and one or more image recognition operations are performed by a microprocessor to match the Braille image to a textural character corresponding to the Braille image. The textural character is converted to an audio waveform. The audio waveform is transmitted to a speaker. The speaker generates a sound representative of a spoken word corresponding to the textural character.