A selectional auto-encoder approach for document image binarization

作者：

Highlights：

• A selectional autoencoder approach for document image binarization is studied.

• The neural network is devoted to learning an image-to-image binarization.

• Comprehensive experimentation with datasets of different typology is presented.

• Results demonstrate that the approach is able to outperform the state of the art.

摘要

•A selectional autoencoder approach for document image binarization is studied.•The neural network is devoted to learning an image-to-image binarization.•Comprehensive experimentation with datasets of different typology is presented.•Results demonstrate that the approach is able to outperform the state of the art.

论文关键词：Binarization,Document analysis,Auto-encoders,Convolutional Neural Networks

论文评审过程：Received 19 September 2017, Revised 31 July 2018, Accepted 27 August 2018, Available online 1 September 2018, Version of Record 10 September 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.08.011