Optical Braille Recognition Using Object Detection CNN
This work addresses the problem of robust Braille text recognition for visually impaired individuals, particularly when using smartphone cameras for image capture.
This paper introduces an optical Braille recognition method utilizing an object detection convolutional neural network to identify entire Braille characters simultaneously. The method demonstrates high performance and accuracy, even when processing images captured by smartphone cameras with page deformations and perspective distortions.
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.