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Braille is an effective way for the visually impaired to learn knowledge andobtain information. Braille image recognition aims to automatically detectBraille dots in the whole Braille image. There is no available public datasetsfor Braille image recognition to push relevant research and evaluatealgorithms. This paper constructs a large-scale Double-Sided Braille Imagedataset DSBI with detailed Braille recto dots, verso dots and Braille cellsannotation. To quickly annotate Braille images, an auxiliary annotationstrategy is proposed, which adopts initial automatic detection of Braille dotsand modifies annotation results by convenient human-computer interactionmethod. This labeling strategy can averagely increase label efficiency by sixtimes for recto dots annotation in one Braille image. Braille dots detection isthe core and basic step for Braille image recognition. This paper alsoevaluates some Braille dots detection methods on our dataset DSBI and gives thebenchmark performance of recto dots detection. We have released our Brailleimages dataset on the GitHub website.

DSBI: Double-Sided Braille Image Dataset and Algorithm Evaluation for Braille Dots Detection