Braille Character Segmentation Algorithm Based on Gaussian Diffusion

Abstract

Optical braille recognition methods typically employ existing target detection models or segmentation models for the direct detection and recognition of braille characters in original braille images. However, these methods need improvement in accuracy and generalizability, especially in densely dotted braille image environments. This paper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithm based on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. This is applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining the central coordinates of the braille convex dots. The second stage involves constructing a braille grid using traditional post-processing algorithms to recognize braille character information. Experimental results demonstrate that this framework exhibits strong robustness and effectiveness in detecting braille dots and recognizing braille characters in complex double-sided braille image datasets. The framework achieved an F1 score of 99.89% for Braille dot detection and 99.78% for Braille character recognition. Compared to the highest accuracy in existing methods, these represent improvements of 0.08% and 0.02%, respectively.