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Writer Adaptation with Style Transfer Mapping
July 2013 (vol. 35 no. 7)
pp. 1773-1787
Xu-Yao Zhang, Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China
Cheng-Lin Liu, Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China
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.
Index Terms:
Prototypes,Hidden Markov models,Handwriting recognition,Accuracy,Adaptation models,Training,Context,handwriting recognition,Writer adaptation,style transfer mapping
Citation:
Xu-Yao Zhang, Cheng-Lin Liu, "Writer Adaptation with Style Transfer Mapping," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 7, pp. 1773-1787, July 2013, doi:10.1109/TPAMI.2012.239
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