HanFont: large-scale adaptive Hangul font recognizer using CNN and font clustering

Yang, Jinhyeok; Kim, Heebeom; Kwak, Hyobin; Kim, In Jung

doi:10.1007/s10032-019-00337-w

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Accuracy(top-1) Accuracy(top-5) CalliNet [14] 65.37 92.50 Dropout [9] 72.33 94.87 Hanfont [13] 76.86 95.88 DropRegion [4] 85…”

Section: Methodsmentioning

confidence: 99%

“…Zhang et.al [14]proposed a method on Chinese calligraphy styles which combines Squeeze-and-excitation block and Haar transform layer in Convolution networks. Yang et.al [13]proposed a Hangul font cluster recognizer to address the issues caused by indistinguishable fonts and untrained new fonts. Goel N et al [2] combines the recent few-shot learning technique like prototype network with font recognition to achieve the effect of identifying new fonts with very few annotated samples per font.…”

Section: Font Recognitionmentioning

confidence: 99%

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HENet: Forcing a Network to Think More for Font Recognition

Chen¹,

Mu²,

Xu³

et al. 2021

Preprint

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Although lots of progress were made in Text Recognition /OCR in recent years, the task of font recognition is remaining challenging.The main challenge lies in the subtle difference between these similar fonts, which is hard to distinguish. This paper proposes a novel font recognizer with a pluggable module solving the font recognition task. The pluggable module hides the most discriminative accessible features and forces the network to consider other complicated features to solve the hard examples of similar fonts, called HE Block. Compared with the available public font recognition systems, our proposed method does not require any interactions at the inference stage. Extensive experiments demonstrate that HENet achieves encouraging performance, including on character-level dataset Explor all and word-level dataset AdobeVFR.

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“…Accuracy(top-1) Accuracy(top-5) CalliNet [14] 65.37 92.50 Dropout [9] 72.33 94.87 Hanfont [13] 76.86 95.88 DropRegion [4] 85…”

Section: Methodsmentioning

confidence: 99%

Section: Font Recognitionmentioning

confidence: 99%

HENet: Forcing a Network to Think More for Font Recognition

Chen¹,

Mu²,

Xu³

et al. 2021

Preprint

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show abstract

“…Therefore, we believe that utilizing a method that recognizes fonts and recommends similar fonts based on stroke elements, rather than analyzing the entire image, can potentially yield better performance. The study on Hangul font clustering and recommendation is conducted by [14]. The study proposes a methodology that combines Convolutional Neural Networks (CNN) and font clustering techniques to achieve accurate and efficient recognition of Hangul characters across a wide range of fonts.…”

Section: Related Workmentioning

confidence: 99%

Definition and Automatic Extraction Performance Analysis of Stroke Elements in the English Alphabet

Lim,

Lee,

Song

2024

IEEE Access

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Fonts are a critical element that determines the perception of any medium. To ensure consistent and culturally appropriate font selection across diverse language groups, a multilingual font matching system is currently in development. This research focuses on leveraging the latest advancements in machine learning and computer vision to deeply understand font characteristics and enhance the accuracy of multilingual font matching. Utilizing the 'stroke elements' of fonts is crucial for this matching, building upon the successful development of a method to calculate similarity between Korean fonts in previous studies. We have applied this approach to the English alphabet, defining distinctive 'stroke elements' and developing a deep learning model for their automatic extraction. Additionally, we evaluate the performance of this stroke element extraction model and discuss strategies to further improve extraction accuracy. This groundwork establishes the basis for multilingual font matching and enables the recommendation of similar fonts using the 'stroke elements' of the English alphabet. INDEX TERMSArtificial neural networks, deep learning, diverse font styles, fonts, object extraction model I. INTRODUCTION

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