DeepFont: Identify your font from an image

Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


As font is one of the core design concepts, automatic font identification and similar font suggestion from an image or photo has been on the wish list of many designers. We study the Visual Font Recognition (VFR) problem [4], and advance the state-of-The-Art remarkably by developing the DeepFont system. First of all, we build up the first avail-able large-scale VFR dataset, named AdobeVFR, consisting of both labeled synthetic data and partially labeled real-world data. Next, to combat the domain mismatch between available training and testing data, we introduce a Convo-lutional Neural Network (CNN) decomposition approach, using a domain adaptation technique based on a Stacked Convolutional Auto-Encoder (SCAE) that exploits a large corpus of unlabeled real-world text images combined with synthetic data preprocessed in a specific way. Moreover, we study a novel learning-based model compression approach, in order to reduce the DeepFont model size without sacrific-ing its performance. The DeepFont system achieves an ac-curacy of higher than 80% (top-5) on our collected dataset, and also produces a good font similarity measure for font selection and suggestion. We also achieve around 6 times compression of the model without any visible loss of recog-nition accuracy.

Original languageEnglish (US)
Title of host publicationMM 2015 - Proceedings of the 2015 ACM Multimedia Conference
PublisherAssociation for Computing Machinery
Number of pages9
ISBN (Electronic)9781450334594
StatePublished - Oct 13 2015
Event23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia
Duration: Oct 26 2015Oct 30 2015

Publication series

NameMM 2015 - Proceedings of the 2015 ACM Multimedia Conference


Other23rd ACM International Conference on Multimedia, MM 2015


  • Deep Learning
  • Domain Adaptation
  • Model Compression
  • Visual Font Recognition

ASJC Scopus subject areas

  • Media Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software


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