  This paper proposes an optical Braille recognition method that uses an object
detection convolutional neural network to detect whole Braille characters at
once. The proposed algorithm is robust to the deformation of the page shown in
the image and perspective distortions. It makes it usable for recognition of
Braille texts being shoot on a smartphone camera, including bowed pages and
perspective distorted images. The proposed algorithm shows high performance and
accuracy compared to existing methods. We also introduce a new "Angelina
Braille Images Dataset" containing 240 annotated photos of Braille texts. The
proposed algorithm and dataset are available at GitHub.
