Degraded document image binarization based on U-Net and transfer learning

GuoChang He

doi:10.1117/12.2600379

1 June 2021 Degraded document image binarization based on U-Net and transfer learning

GuoChang He

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 1184806 (2021) https://doi.org/10.1117/12.2600379
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

Datasets of degraded document image are small, making the network unable to be fully trained or easily over-fitting. And single-convolution network has poor generalization ability. These factors lead to an unsatisfactory binarization performance. This paper proposes a degraded document image binarization method based on U-Net and transfer learning to solve these problems. U-Net is used as our model’s backbone for its good performance in small datasets. The common transfer learning network models ResNet is utilized as the pre-training encoder to improve the generalization ability of our model. Then we establish different decoder network structures for the characteristics of different encoders. In addition, different from conventional U-Net, the convolutional layer output of downsampling is made as the skip connection object to be superimposed with the input of upsampling in our models, so the upsampling layers can better restore the details of document images. In this way, we improve the convergence and generalization ability to get a better binarization performance.

Citation Download Citation

GuoChang He "Degraded document image binarization based on U-Net and transfer learning", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 1184806 (1 June 2021); https://doi.org/10.1117/12.2600379

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