Abstract
Despite numerous fonts already being designed and easily available online, the desire for new fonts seems to be endless. Previous methods focused on extracting style, shape, and stroke information from a large set of fonts, or transforming and interpolating existing fonts to create new fonts. The drawback of these methods is that generated fonts look-alike fonts of training data. As fonts are created from human handwriting documents, they have uncertainty and randomness incorporated into them, giving them a more authentic feel than standard fonts. Handwriting, like a fingerprint, is unique to each individual. In this paper, we have proposed GAN-based models that automate the entire font generation process, removing the labor involved in manually creating a new font. We extracted data from single-author handwritten documents and developed and trained class-conditioned DCGAN models to generate fonts that mimic the author’s handwriting style.
Supported by I-Hub foundation for Cobotics.
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The present research is partially funded by the I-Hub foundation for Cobotics (Technology Innovation Hub of IIT-Delhi set up by the Department of Science and Technology, Govt. of India).
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Kalingeri, R., Kushwaha, V., Kala, R., Nandi, G.C. (2023). Synthesis of Human-Inspired Intelligent Fonts Using Conditional-DCGAN. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_55
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