2018/11/27 by Renqiang Li, Hong Liu, Li, Renqiang +5
Computer Science · Neuroscience · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Hand Gesture Recognition Systems #Interactive and Immersive Displays #Tactile and Sensory Interactions #cs.CV
paper · pdf · doi:10.48550/arxiv.1811.10893
openalex publication_date 2018/11/27 · arxiv created 2019/04/02 · arxiv updated 2019/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.