DocumentCode :
64030
Title :
Writer Adaptation with Style Transfer Mapping
Author :
Xu-Yao Zhang ; Cheng-Lin Liu
Author_Institution :
Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China
Volume :
35
Issue :
7
fYear :
2013
fDate :
Jul-13
Firstpage :
1773
Lastpage :
1787
Abstract :
Adapting a writer-independent classifier toward the unique handwriting style of a particular writer has the potential to significantly increase accuracy for personalized handwriting recognition. This paper proposes a novel framework of style transfer mapping (STM) for writer adaptation. The STM is a writer-specific class-independent feature transformation which has a closed-form solution. After style transfer mapping, the data of different writers are projected onto a style-free space, where the writer-independent classifier needs no change to classify the transformed data and can achieve significantly higher accuracy. The framework of STM can be combined with different types of classifiers for supervised, unsupervised, and semi-supervised adaptation, where writer-specific data can be either labeled or unlabeled and need not cover all classes. In this paper, we combine STM with the state-of-the-art classifiers for large-category Chinese handwriting recognition: learning vector quantization (LVQ) and modified quadratic discriminant function (MQDF). Experiments on the online Chinese handwriting database CASIA-OLHWDB demonstrate that STM-based adaptation is very efficient and effective in improving classification accuracy. Semi-supervised adaptation achieves the best performance, while unsupervised adaptation is even better than supervised adaptation. On handwritten text data, semi-supervised adaptation achieves error reduction rates 31.95 and 25.00 percent by LVQ and MQDF, respectively.
Keywords :
feature extraction; handwritten character recognition; image classification; learning (artificial intelligence); vector quantisation; visual databases; LVQ; MQDF; STM; handwriting style; large-category Chinese handwriting recognition; learning vector quantization; modified quadratic discriminant function; online Chinese handwriting database CASIA-OLHWDB; personalized handwriting recognition; semisupervised adaptation; style transfer mapping; style-free space; unsupervised adaptation; writer adaptation; writer-independent classifier; writer-specific class-independent feature transformation; Accuracy; Adaptation models; Context; Handwriting recognition; Hidden Markov models; Prototypes; Training; Writer adaptation; handwriting recognition; style transfer mapping; Algorithms; Artificial Intelligence; Databases, Factual; Handwriting; Humans; Image Processing, Computer-Assisted; Pattern Recognition, Automated;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2012.239
Filename :
6341757
Link To Document :
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