Back to articles
Articles
Volume: 29 | Article ID: art00014
Image
Semi-supervised Learning Feature Representation for Historical Chinese Character Recognition
  DOI :  10.2352/ISSN.2470-1173.2017.2.VIPC-411  Published OnlineJanuary 2017
Abstract

Historical Chinese character recognition has been suffering from the problem of lacking sufficient labeled training samples. An Semi-supervised learning method based on Convolutional Neural Network (CNN) for historical Chinese character recognition is proposed in this paper. We use traditional feature extraction method to extract features from the unlabeled sample sets at first; then according to the distance between the extracted features, samples pairs are constructed; With the constructed pairs, a Siamese network S is trained; The network structure and weights of model S are used to initialize another CNN model T. The model T is then fine-tuned by a few labeled historical Chinese character samples, and used for final evaluation. Experimental results show that the proposed method is effective.

Subject Areas :
Views 87
Downloads 1
 articleview.views 87
 articleview.downloads 1
  Cite this article 

Xiaoyi Yu, Wei Fan, Jun Sun, Satoshi Naoi, "Semi-supervised Learning Feature Representation for Historical Chinese Character Recognitionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Visual Information Processing and Communication VIII,  2017,  pp 73 - 77,  https://doi.org/10.2352/ISSN.2470-1173.2017.2.VIPC-411

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2017
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology