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An Autoencoder Based ASCII Art Generator

Published: 13 July 2023 Publication History

Abstract

ASCII art is a way to represent an image with character shapes. It is common to carry ASCII art instead of displaying image files on Internet bulletin boards. Multibyte encodings contain various characters that are useful to shape an image. The essential idea required to get ASCII art is to approximate color distribution at the portion of a target image to a character shape. In this study, we make a machine learning model that learns the shapes of characters in a multibyte encoding to convert a partial image of a target image to a font image.

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  • (2024)A Trial to Generate ASCII Art Images by Use of Machine Learning Models機械学習をつかったアスキーアート作成の試みJournal of Japan Society of Kansei Engineering10.5057/kansei.22.4_19122:4(191-196)Online publication date: 31-Dec-2024

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  1. An Autoencoder Based ASCII Art Generator

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    ICIIT '23: Proceedings of the 2023 8th International Conference on Intelligent Information Technology
    February 2023
    310 pages
    ISBN:9781450399616
    DOI:10.1145/3591569
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 13 July 2023

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    Author Tags

    1. ASCII art synthesis
    2. K-nearest neighborhood
    3. autoencoder
    4. deep learning

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    • (2024)A Trial to Generate ASCII Art Images by Use of Machine Learning Models機械学習をつかったアスキーアート作成の試みJournal of Japan Society of Kansei Engineering10.5057/kansei.22.4_19122:4(191-196)Online publication date: 31-Dec-2024

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