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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Azadi, S., Fisher, M., Kim, V.G., Wang, Z., Shechtman, E., Darrell, T.: Multi-content GAN for few-shot font style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7564–7573 (2018)
Chen, G., et al.: Large-scale visual font recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3598–3605 (2014)
Chen, H., Tsai, S.S., Schroth, G., Chen, D.M., Grzeszczuk, R., Girod, B.: Robust text detection in natural images with edge-enhanced maximally stable extremal regions. In: 2011 18th IEEE International Conference on Image Processing, pp. 2609–2612. IEEE (2011)
Jung, M.C., Shin, Y.C., Srihari, S.N.: Multifont classification using typographical attributes. In: Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR 1999 (Cat. No. PR00318), pp. 353–356. IEEE (1999)
Li, Y., Naoi, S., Cheriet, M., Suen, C.Y.: A segmentation method for touching italic characters. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2, pp. 594–597. IEEE (2004)
Ma, H., Doermann, D.S.: Gabor filter based multi-class classifier for scanned document images. In: ICDAR, vol. 3, p. 968. Citeseer (2003)
Ramanathan, R., Soman, K., Thaneshwaran, L., Viknesh, V., Arunkumar, T., Yuvaraj, P.: A novel technique for English font recognition using support vector machines. In: 2009 International Conference on Advances in Recent Technologies in Communication and Computing, pp. 766–769. IEEE (2009)
Solli, M., Lenz, R.: FyFont: find-your-font in large font databases. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 432–441. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73040-8_44
Sun, H.M.: Multi-linguistic optical font recognition using stroke templates. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 2, pp. 889–892. IEEE (2006)
Wang, Z., et al.: Deepfont: identify your font from an image. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 451–459. ACM (2015)
Zhu, Y., Tan, T., Wang, Y.: Font recognition based on global texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1192–1200 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-41299-9_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41298-2
Online ISBN: 978-3-030-41299-9
eBook Packages: Computer ScienceComputer Science (R0)