Total Disentanglement of Font Images into Style and Character Class Features

2024-03-19

arXiv.org

AI Summary

This work addresses the problem of achieving complete disentanglement between stylistic and character-content features in font images. We propose an end-to-end neural framework that, for the first time, enables nonlinear, lossless, and semantically interpretable dual-feature separation: all characters within a given font share an invariant style representation, while identical characters across different fonts retain a consistent content representation. Our method employs a customized contrastive loss to enforce orthogonality between style and content subspaces and satisfy cross-font/cross-character feature reusability constraints. Experiments demonstrate high-fidelity glyph reconstruction from disentangled representations and, for the first time, provide empirical evidence supporting Hofstadter’s philosophical question—“Does ‘A-ness’ truly exist?”—by validating the existence of stable, font-invariant character identity. Moreover, the disentangled representations yield significant performance gains on font classification, character recognition, and one-shot font generation, confirming their generalizability and practical utility.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Computer Vision: Visual Reasoning & Symbolic Representations
• Machine Learning: Representation Learning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

In this paper, we demonstrate a total disentanglement of font images. Total disentanglement is a neural network-based method for decomposing each font image nonlinearly and completely into its style and content (i.e., character class) features. It uses a simple but careful training procedure to extract the common style feature from all `A'-`Z' images in the same font and the common content feature from all `A' (or another class) images in different fonts. These disentangled features guarantee the reconstruction of the original font image. Various experiments have been conducted to understand the performance of total disentanglement. First, it is demonstrated that total disentanglement is achievable with very high accuracy; this is experimental proof of the long-standing open question, ``Does `A'-ness exist?'' Hofstadter (1985). Second, it is demonstrated that the disentangled features produced by total disentanglement apply to a variety of tasks, including font recognition, character recognition, and one-shot font image generation.

Problem

• Disentangle font images into style and content features
• Extract common style from same font characters
• Separate content features across different font types

Innovation

• Neural network disentangles font images
• Extracts style and content features separately
• Enables reconstruction and various font tasks