Style transfer matrix learning for writer adaptation

  title={Style transfer matrix learning for writer adaptation},
  author={Xu-Yao Zhang and Cheng-Lin Liu},
  journal={CVPR 2011},
In this paper, we propose a novel framework of style transfer matrix (STM) learning to reduce the writing style variation in handwriting recognition. After writer-specific style transfer learning, the data of different writers is projected onto a style-free space, where a writer independent classifier can yield high accuracy. We combine STM learning with a specific nearest prototype classifier: learning vector quantization (LVQ) with discriminative feature extraction (DFE), where both the… CONTINUE READING
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