Fast Text Placement Scheme for ASCII Art Synthesis - Moonjun Chung; Taesoo Kwon (IEEE Access, 2022)
This study suggests an algorithm that creates ASCII art from a binary image. Our approach aims to generate ASCII art in a short period of time using multi-threaded local optimizations for a text placement method instead of a global optimization. To generate ASCII art from various images, the original image is first converted into a thinned black and white image suitable for generating ASCII art. We then extract the pixel orientations from the input image and introduce a character similarity scheme that considers these orientations. We also propose a novel text placement algorithm to complete ASCII art in a swift manner. Our final system suggested here can generate ASCII art using a variety of proportional fonts. The results of the experiments of this study show that the suggested system can generate ASCII art much faster than existing state-of-the-art techniques using proportional fonts.