Font Recognition in Natural Images via Transfer Learning

Yizhi Wang Zhouhui Lian Yingmin Tang Jianguo Xiao

 Institute of Computer Science and Technology, Peking University, Beijing, P.R.China

 {wangyizhi, lianzhouhui, tangyingmin, xiaojianguo}


Font recognition is an important and challenging problem in areas of Document Analysis, Pattern Recognition and Computer Vision. In this paper, we try to handle a tougher task that aims to accurately recognize the font styles of texts in natural images by proposing a novel method based on deep learning and transfer learning. Major contributions of this paper are threefold: First, we develop a fast and scalable system to synthesize huge amounts of natural images containing texts in various fonts and styles, which are then utilized to train the deep neural network for font recognition. Second, we design a transfer learning scheme to alleviate the domain mismatch between synthetic and real-world text images. Thus, large numbers of unlabeled text images can be adopted to markedly enhance the discrimination and robustness of our font classifier. Third, we build a benchmarking database which consists of numerous labeled natural images containing Chinese characters in 48 fonts. As far as we know, it is the first publicly-available dataset for font recognition of Chinese characters in natural images.



Snapshot for paper Font Recognition in Natural Images via Transfer Learning


Yizhi Wang, Zhouhui Lian, Yingmin Tang, Jianguo Xiao



paper [ Pre-print] data [ VRFWild-CHS Dataset 0.88GB]



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