Abstract
Identification of font in document images has many applications in modern character recognition systems. Visual font recognition task is a challenging yet popular problem in pattern recognition as many designers try to identify a font for their designs from available images. However, the identification problem is particularly difficult because of the large number of probable fonts that can be used for a particular design. Moreover, there can be multiple fonts in a database which contain similar visual features; therefore, it is difficult to identify the exact font class. In this paper, we explore the font recognition problem for a database of 10,000 fonts using convolutional neural network (CNN) architecture. To the best of our knowledge, no previous approach has explored the font identification problem with these many classes. We performed extensive experiments to quantify our results for synthetic document images as well as natural document images. We have achieved 63.45% top-1 accuracy and 70.76% top-3 accuracy in character level, in addition we also observed 57.18% top-1 accuracy and 62.11% top-3 accuracy in word level even in the presence of rotation and scaling, which demonstrate the effectiveness of the proposed method.
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Ghosh, S., Roy, P., Bhattacharya, S., Pal, U. (2020). Large-Scale Font Identification from Document Images. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_46
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DOI: https://doi.org/10.1007/978-3-030-41299-9_46
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