Character-Independent Font Identification

Haraguchi, Daichi; Harada, Shota; Iwana, Brian Kenji; Shinahara, Yuto; Uchida, Seiichi

doi:10.1007/978-3-030-57058-3_35

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Chen [16] introduced label attributes, designing a specialized algorithm for English font images. Haraguchi [17] utilized CNNs to identify fonts of uppercase English letters. Despite valuable insights for Chinese character font recognition, differences arise due to the complexity of Chinese characters, their high quantity, intricate structures, similarities, diverse font categories, and subtle distinctions within subcategories.…”

Section: A Non-chinese Fonts Recognitionmentioning

confidence: 99%

SMFNet: One-Shot Recognition of Chinese Character Font Based on Siamese Metric Model

Yan,

Zhang

2024

IEEE Access

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This paper focuses on Chinese character font recognition and introduces a novel one-shot leaning model SMFNet for Chinese character font recognition , by thoroughly analyzing the fundamental characteristics of Chinese characters. Due to the considerable quantity and intricate structures of Chinese character fonts, conventional methods for Chinese character font recognition face challenges in meeting the necessary requirements. To address this issue, a siamese metric structure is incorporated as the model framework, in conjunction with coordinate attention modules and multiple loss functions, enabling one-shot font recognition across various categories such as standard printed fonts, soft brush calligraphy fonts, and hard pen calligraphy fonts. The model is trained and tested using the XIKE-CFS Chinese character font style dataset. Experimental results indicate that SMFNet achieves a recognition accuracy of 97.50% on the XIKE-CFS dataset, while also supporting one-shot recognition with an accuracy rate of 92.84%. Furthermore, compared to other prevalent CNN classification models, SMFNet exhibits outstanding performance in terms of parameter quantity and demonstrates excellent capabilities for recognizing new fonts. In summary, this work provides innovative perspectives and methodologies for addressing challenges in the field of Chinese character font recognition.INDEX TERMS Chinese character font, coordinate attention, one-shot recognition, siamese network.

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Section: A Non-chinese Fonts Recognitionmentioning

confidence: 99%

SMFNet: One-Shot Recognition of Chinese Character Font Based on Siamese Metric Model

Yan,

Zhang

2024

IEEE Access

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“…DeepFont has been proposed [25] for this purpose. Haraguchi et al [9] tackle the font identification task as a matching problem.…”

Section: Font Style and Impressionmentioning

confidence: 99%

Shared Latent Space of Font Shapes and Their Noisy Impressions

Kang¹,

Haraguchi²,

Matsuda³

et al. 2021

Preprint

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We have specific impressions from the style of a typeface (font), suggesting that there are correlations between font shape and its impressions. Based on this hypothesis, we realize a shared latent space where a font shape image and its impression words are embedded in a cross-modal manner. This latent space is useful to understand the style-impression correlation and generate font images by specifying several impression words. Experimental results with a large style-impression dataset prove that it is possible to accurately realize the shared latent space, especially for shape-relevant impression words, and then use the space to generate font images with various impressions.

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Local Style Awareness of Font Images

Haraguchi

Uchida

2023

Lecture Notes in Computer Science

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Character-Independent Font Identification

Cited by 6 publications

References 28 publications

SMFNet: One-Shot Recognition of Chinese Character Font Based on Siamese Metric Model

SMFNet: One-Shot Recognition of Chinese Character Font Based on Siamese Metric Model

Shared Latent Space of Font Shapes and Their Noisy Impressions

Local Style Awareness of Font Images

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