Unsupervised language model adaptation for handwritten Chinese text recognition

作者：

Highlights：

• We propose an unsupervised language model (LM) adaptation framework for handwritten Chinese text recognition.

• We use a two-pass recognition strategy with a pre-defined multi-domain LM set.

• The adaptive LM is dynamically generated by three methods of model selection, model combination and model reconstruction.

• We compress the LM set by split vector quantization and principal component analysis.

摘要

Highlights•We propose an unsupervised language model (LM) adaptation framework for handwritten Chinese text recognition.•We use a two-pass recognition strategy with a pre-defined multi-domain LM set.•The adaptive LM is dynamically generated by three methods of model selection, model combination and model reconstruction.•We compress the LM set by split vector quantization and principal component analysis.

论文关键词：Character string recognition,Chinese handwriting recognition,Unsupervised language model adaptation,Language model compression

论文评审过程：Received 13 December 2012, Revised 17 September 2013, Accepted 19 September 2013, Available online 27 September 2013.

论文官网地址：https://doi.org/10.1016/j.patcog.2013.09.015