DSBI: Double-Sided Braille Image Dataset and Algorithm Evaluation for Braille Dots Detection
This work addresses a domain-specific problem for visually impaired individuals by providing a foundational dataset and benchmark for Braille dots detection, though it is incremental in nature.
The paper tackles the lack of public datasets for Braille image recognition by constructing a large-scale Double-Sided Braille Image dataset (DSBI) with detailed annotations, and it introduces an auxiliary annotation strategy that increases labeling efficiency by six times for recto dots.
Braille is an effective way for the visually impaired to learn knowledge and obtain information. Braille image recognition aims to automatically detect Braille dots in the whole Braille image. There is no available public datasets for Braille image recognition to push relevant research and evaluate algorithms. This paper constructs a large-scale Double-Sided Braille Image dataset DSBI with detailed Braille recto dots, verso dots and Braille cells annotation. To quickly annotate Braille images, an auxiliary annotation strategy is proposed, which adopts initial automatic detection of Braille dots and modifies annotation results by convenient human-computer interaction method. This labeling strategy can averagely increase label efficiency by six times for recto dots annotation in one Braille image. Braille dots detection is the core and basic step for Braille image recognition. This paper also evaluates some Braille dots detection methods on our dataset DSBI and gives the benchmark performance of recto dots detection. We have released our Braille images dataset on the GitHub website.