Automatic Detection of Font Size Straight from Run Length Compressed Text Documents

Javed, Mohammed; Nagabhushan, P.; Chaudhuri, B. B.

doi:10.48550/arxiv.1402.4388

Cited by 1 publication

(1 citation statement)

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“…Using this method, it is possible to build a latent space that groups together characters from similar fonts. Other existing methods, such as Adobe DeepFont Wang et al [2015] or Javed et al [2014], Zhu et al [2001], Ioffe and Szegedy [2015], Zramdini and Ingold [1998], Bataineh et al [2012], Gao et al [2008], focus on font classification and font recognition rather than font feature analysis.…”

Section: Typographic Analysismentioning

confidence: 99%

Toward Automatic Typography Analysis: Serif Classification and Font Similarities

Wasim,

Collaud,

Défayes

et al. 2024

Journal of Data Mining &Amp; Digital Humanities

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Whether a document is of historical or contemporary significance, typography plays a crucial role in its composition. From the early days of modern printing, typographic techniques have evolved and transformed, resulting in changes to the features of typography. By analyzing these features, we can gain insights into specific time periods, geographical locations, and messages conveyed through typography. Therefore, in this paper, we aim to investigate the feasibility of training a model to classify serif typeswithout knowledge of the font and character. We also investigate how to train a vectorial-based image model able to group together fonts with similar features. Specifically, we compare the use of state-of-theart image classification methods, such as the EfficientNet-B2 and the Vision Transformer Base model with different patch sizes, and the state-of-the-art fine-grained image classification method, TransFG, on the serif classification task. We also evaluate the use of the DeepSVG model to learn to group fonts with similar features. Our investigation reveals that fine-grained image classification methods are better suited for the serif classification tasks and that leveraging the character labels helps to learn more meaningful font similarities.This repository contains: - Paper published in the Journal of data mining and digital humanities:WasimEtAl_Toward_Automatic_Typography_Analysis__Serif_Classification_and_Font_Similarities.pdf - Two datasets: The first for serif classification consisting of 126666 training and 2914 font-independent testing images in raster format. The second dataset for svg based similarity learning consists of 124010 training and 2914 font-independent testing images. The images have been categorized into sans-serif, linear-serif, slab-serif, and triangular serif by a designer at the EPFL+ECAL Labfont_serif_dataset.zipfont_svg_dataset.zip - model weights:serif_B_16_long_checkpoint.bin: refers to TransFG ViT B/16 Backendserif_B_32_long_checkpoint.bin: refers to TransFG ViT B/32 BackendViT-B_16.npz ViT-B_32.npz are publicly available at: https://github.com/TACJu/TransFGand were used for training TransFG ViT B/16 Backend and TransFG ViT B/32 Backend - code:https://github.com/TalalWasim/GEST-Serif

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