An automated approach to mitigate transcription errors in braille texts for the Portuguese language

TL;DR

This paper introduces an automated method to reduce transcription errors in Portuguese braille texts using a selection function and dictionaries, validated on noisy synthetic images, improving robustness over standard approaches.

Contribution

The paper presents a novel selection function combined with dictionaries to enhance braille transcription accuracy under noisy conditions, addressing a key challenge in computer vision-based braille recognition.

Findings

The proposed method outperforms standard approaches in noisy environments.

Validation on synthetic images shows increased robustness and accuracy.

The approach supports adaptable solutions for real-world braille transcription challenges.

Abstract

The quota system in Brazil made it possible to include blind students in higher education. Teachers' lack of knowledge about the braille system can represent a barrier between them and students who use it for writing and reading. Computer-vision-based transcription solutions represent mechanisms for reducing understanding restrictions on this system. However, such tools face nuisances inherent to image processing systems, e.g., illumination, noise, and scale, harming the result. This paper presents an automated approach to mitigate transcription errors in braille texts for the Portuguese language. We propose a selection function, combined with dictionaries, that provides the best correspondence of words based on their braille representation. We validated our proposal on a dataset of synthetic images by submitting them to different noise levels and testing the proposal's robustness. Experimental results confirm the effectiveness of the solution compared to a standard approach. As a contribution of this paper, we expect to provide a method to support robust and adaptable solutions to real use conditions.