TY - GEN
T1 - Cascading modular U-nets for document image binarization
AU - Kang, Seokjun
AU - Iwana, Brian Kenji
AU - Uchida, Seiichi
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by JSPS KAKENHI Grant Number JP17K19402 and JP17H06100.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - In recent years, U-Net has achieved good results in various image processing tasks. However, conventional U-Nets need to be re-trained for individual tasks with enough amount of images with ground-truth. This requirement makes U-Net not applicable to tasks with small amounts of data. In this paper, we propose to use 'modular' U-Nets, each of which is pre-trained to perform an existing image processing task, such as dilation, erosion, and histogram equalization. Then, to accomplish a specific image processing task, such as binarization of historical document images, the modular U-Nets are cascaded with inter-module skip connections and fine-tuned to the target task. We verified the proposed model using the Document Image Binarization Competition (DIBCO) 2017 dataset.
AB - In recent years, U-Net has achieved good results in various image processing tasks. However, conventional U-Nets need to be re-trained for individual tasks with enough amount of images with ground-truth. This requirement makes U-Net not applicable to tasks with small amounts of data. In this paper, we propose to use 'modular' U-Nets, each of which is pre-trained to perform an existing image processing task, such as dilation, erosion, and histogram equalization. Then, to accomplish a specific image processing task, such as binarization of historical document images, the modular U-Nets are cascaded with inter-module skip connections and fine-tuned to the target task. We verified the proposed model using the Document Image Binarization Competition (DIBCO) 2017 dataset.
UR - http://www.scopus.com/inward/record.url?scp=85079859813&partnerID=8YFLogxK
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U2 - 10.1109/ICDAR.2019.00113
DO - 10.1109/ICDAR.2019.00113
M3 - Conference contribution
AN - SCOPUS:85079859813
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 675
EP - 680
BT - Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
PB - IEEE Computer Society
T2 - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
Y2 - 20 September 2019 through 25 September 2019
ER -