Braille Detection Model Based on Foreground Attention and Semantic Learning

This paper presents BrailleNet, an enhanced one-stage anchor-based object detection model for Braille character recognition, incorporating foreground attention and semantic learning. BrailleNet is validated using two new datasets: mobile phone live shot Braille and chart Braille. Experimental results show BrailleNet's superior performance on both public and introduced datasets.