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Fly-LeNet: A deep learning-based framework for converting multilingual braille images.

Abdulmalik Al-Salman¹, Amani AlSalman²

¹Computer Science Department, King Saud University, Riyadh, Saudi Arabia.

|February 23, 2024

Summary

A new Optical Braille Recognition (OBR) system, Fly-LeNet, converts braille images into multilingual text. This deep learning approach enhances communication and braille learning for sighted individuals.

Keywords:

Braille Deep learning (DL)Multilingual braille images

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Area of Science:

Computer Science
Artificial Intelligence
Assistive Technology

Background:

Braille-assistive technologies improve accessibility for blind individuals.
Current Optical Braille Recognition (OBR) systems lack multilingual conversion capabilities.
This limits sighted individuals' ability to learn braille for self-study.

Purpose of the Study:

To develop a novel system for converting braille images into multilingual text.
To address the limitations of existing OBR systems in multilingual braille translation.
To facilitate braille learning and communication for sighted individuals.

Main Methods:

A segmentation and deep learning approach named Fly-LeNet was proposed.
The method incorporates image acquisition, preprocessing, and segmentation using Mayfly optimization and thresholding.
A braille multilingual mapping step and the LeNet-5 deep learning model for braille cell recognition are utilized.

Main Results:

Fly-LeNet achieved high classification accuracies of 99.77% and 99.80% on two distinct braille image datasets.
Experiments were conducted on datasets containing braille signs for alphabets, numbers, and punctuation.
The model demonstrated robust performance in recognizing braille cells.

Conclusions:

The Fly-LeNet system shows significant potential for multilingual braille transformation.
This technology can enhance communication between blind and sighted individuals.
It offers a valuable tool for self-learning braille and promoting inclusivity.