Abstract
There are a countless number of fonts with various shapes and styles. In addition, there are many fonts that only have subtle differences in features. Due to this, font identification is a difficult task. In this paper, we propose a method of determining if any two characters are from the same font or not. This is difficult due to the difference between fonts typically being smaller than the difference between alphabet classes. Additionally, the proposed method can be used with fonts regardless of whether they exist in the training or not. In order to accomplish this, we use a Convolutional Neural Network (CNN) trained with various font image pairs. In the experiment, the network is trained on image pairs of various fonts. We then evaluate the model on a different set of fonts that are unseen by the network. The evaluation is performed with an accuracy of 92.27%. Moreover, we analyzed the relationship between character classes and font identification accuracy.
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Notes
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Throughout this paper, we assume that the pairs come from different character classes. This is simply because our font identification becomes a trivial task for the pairs of the same character class (e.g., ‘A’); if the two images are exactly the same, they are the same font; otherwise, they are different.
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Acknowledgment
This work was supported by JSPS KAKENHI Grant Number JP17H06100.
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Appendix a Font Identification Using a Dataset with Less Fancy Fonts
Appendix a Font Identification Using a Dataset with Less Fancy Fonts
The dataset used in the above experiment contains many fancy fonts and thus there was a possibility that our evaluation might overestimate the font identification performance; this is because fancy fonts are sometimes easy to be identified by their particular appearance. We, therefore, use another font dataset, called the Adobe Font Folio 11.1Footnote 3. From this font set, we selected 1,132 fonts, which are comprised of 511 Serif, 314 Sans Serif, 151 Serif-Sans Hybrid, 74 Script, 61 Historical Script, and (only) 21 Fancy fonts. Note that this font type classification for the 1,132 fonts is given by [1]. We used the same neural network trained by the dataset of Sect. 4.1, i.e., trained with the fancy font dataset and tested on the Adobe dataset. Note that for the evaluation, 367,900 positive pairs and 367,900 negative pairs are prepared using the 1,132 fonts. Using the Adobe fonts as the test, the identification accuracy was 88.33 ± 0.89%. This was lower than \(92.27\%\) of the original dataset. However, considering the fact that formal fonts are often very similar to each other, we can still say that the character-independent font identification is possible even for the formal fonts.
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Haraguchi, D., Harada, S., Iwana, B.K., Shinahara, Y., Uchida, S. (2020). Character-Independent Font Identification. In: Bai, X., Karatzas, D., Lopresti, D. (eds) Document Analysis Systems. DAS 2020. Lecture Notes in Computer Science(), vol 12116. Springer, Cham. https://doi.org/10.1007/978-3-030-57058-3_35
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