Semi-supervised learning for character recognition in historical archive documents

作者：

Highlights：

• We present semi-supervised labeling strategies that are able to considerably reduce the human effort.

• Two different methods to label and later recognize characters in collections of historical archive documents are proposed.

• A realistic application dealing with handwritten historical weather reports is introduced.

• Both methods are evaluated on the MNIST database of handwritten digits and the historical weather reports.

摘要

Highlights•We present semi-supervised labeling strategies that are able to considerably reduce the human effort.•Two different methods to label and later recognize characters in collections of historical archive documents are proposed.•A realistic application dealing with handwritten historical weather reports is introduced.•Both methods are evaluated on the MNIST database of handwritten digits and the historical weather reports.

论文关键词：Character recognition,Semi-supervised learning,Historical documents

论文评审过程：Available online 31 July 2013.

论文官网地址：https://doi.org/10.1016/j.patcog.2013.07.013