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This research is carried out to identify the font of an English language using convolution neural network (CNN); the motivation behind this research is to unravel a realistic designer’s issue. It helps a lot to solve the designer’s problem. Typography is an integral feature of a brand or a creative design. While working as a graphic designer, we would often come across the pain of guessing the font/typeface of the requirement from the given reference by just eye-balling it. The main focus is to develop a model to identify the font which we want to use. But again, you just have to guess it. Sometimes, you don’t even know the typeface to guess in the first place. For this, one just has to take an image of the required typeface sample and feed it to the CNN model and it can effectively recognize the font. It scopes across the platforms of web and Android application and also in future looking forward to iOS application. This study endeavors to employ convolution neural network techniques for the purpose of font identification within the context of the English language. The impetus driving this research lies in the aspiration to address a pertinent concern faced by designers in a realistic manner. The elucidation of font selection significantly contributes to the resolution of design-related challenges. Typography constitutes an indispensable facet of brand identity and creative design. In the capacity of a graphic designer, one frequently encounters the vexing task of discerning the appropriate font or typeface from a given reference solely through visual assessment. The primary emphasis centers on the development of a model capable of accurately identifying the desired font. Nevertheless, this task often entails a degree of conjecture. At times, one may lack even the initial knowledge of the typeface to contemplate. To mitigate this, a simple capture of the requisite typeface sample in image format suffices, subsequently provided as input to the CNN model, thus enabling proficient font recognition. The potential applications extend across diverse platforms, encompassing web-based interfaces, Android applications, and a prospective integration into iOS applications in the future.

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Typeface Identification for English Using Convolutional Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Sanket Suthar,
  • Rajnik Katriya,
  • Bijal Dalwadi

摘要

This research is carried out to identify the font of an English language using convolution neural network (CNN); the motivation behind this research is to unravel a realistic designer’s issue. It helps a lot to solve the designer’s problem. Typography is an integral feature of a brand or a creative design. While working as a graphic designer, we would often come across the pain of guessing the font/typeface of the requirement from the given reference by just eye-balling it. The main focus is to develop a model to identify the font which we want to use. But again, you just have to guess it. Sometimes, you don’t even know the typeface to guess in the first place. For this, one just has to take an image of the required typeface sample and feed it to the CNN model and it can effectively recognize the font. It scopes across the platforms of web and Android application and also in future looking forward to iOS application. This study endeavors to employ convolution neural network techniques for the purpose of font identification within the context of the English language. The impetus driving this research lies in the aspiration to address a pertinent concern faced by designers in a realistic manner. The elucidation of font selection significantly contributes to the resolution of design-related challenges. Typography constitutes an indispensable facet of brand identity and creative design. In the capacity of a graphic designer, one frequently encounters the vexing task of discerning the appropriate font or typeface from a given reference solely through visual assessment. The primary emphasis centers on the development of a model capable of accurately identifying the desired font. Nevertheless, this task often entails a degree of conjecture. At times, one may lack even the initial knowledge of the typeface to contemplate. To mitigate this, a simple capture of the requisite typeface sample in image format suffices, subsequently provided as input to the CNN model, thus enabling proficient font recognition. The potential applications extend across diverse platforms, encompassing web-based interfaces, Android applications, and a prospective integration into iOS applications in the future.