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Complex image processing with less data-Document image binarization by integrating multiple pre-trained U-Net modules
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DOI：10.1016/j.patcog.2020.107577.png)
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Abstract
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Artificial neural networks have been shown significant performance in various image-to-image conversion tasks. However, complex conversions often require a large number of images for model training. Therefore, we propose a convolutional model for image-to-image conversions using a pipeline of simpler image processing modules. To verify our proposed approach, we use a document image binarization as the task. Document image binarization is an important process that affects the accuracy of document analysis and recognition. In this paper, we propose a novel document binarization method called Cascading Modular U-Nets (CMU-Nets). CMU-Nets consist of pre-trained modular modules useful for overcoming the problem of a shortage of training images. We also propose a novel cascading scheme for improving overall cascading model performance. We verify the proposed model on all available Document Image Binarization Competition (DIBCO) and the Handwritten-DIBCO (H-DIBCO) datasets. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Convolutional neural network
U-Net
Document image binarization
DIBCO
H-DIBCO
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