A method for transforming raster graphics of mathematical graphs into vector graphics using deep neural networks

Abstract

International standards and organizations are working to provide access to printed and electronic books for the visually impaired. As part of this effort, there is software that reduces the burden of Braille translation by using text OCR and formula OCR. However, there are very few such efforts for image data, and the figures in the literature are translated into Braille by sighted people. In this case, there is a vector format that is accessible to the visually impaired. Due to its characteristics, vector images can be easily redrawn and re-edited, and they can be read by point chart readers and 3D printers, so the use of vector images is recommended. On the other hand, not only printed books but also most electronic books are stored in raster format, and it costs a lot of money for editors to convert raster images to vector images. In this paper, we propose a method to analyze raster images and convert them into vector images using deep learning.