论文标题
使用对象检测CNN识别光学盲文
Optical Braille Recognition Using Object Detection CNN
论文作者
论文摘要
本文提出了一种光学盲文识别方法，该方法使用对象检测卷积神经网络一次检测整个盲文字符。所提出的算法对于图像和透视扭曲中所示的页面的变形是可靠的。它可用于识别在智能手机相机上拍摄的盲文文本，包括鞠躬页面和透视图像扭曲的图像。与现有方法相比，提出的算法显示出高性能和准确性。我们还介绍了一个新的“ Angelina Braille Images数据集”，其中包含240张带注释的盲文文本照片。拟议的算法和数据集可在GitHub上找到。
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.
