Bipolar morphological U-Net for document binarization

Elena Limonova; Dmitry Nikolaev; Vladimir Arlazarov

doi:10.1117/12.2587174

4 January 2021 Bipolar morphological U-Net for document binarization

Elena Limonova, Dmitry Nikolaev, Vladimir Arlazarov

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116050P (2021) https://doi.org/10.1117/12.2587174
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

Deep neural networks are widely used in various AI systems. Many such systems rely on the edge computing concept and try to perform computations on end devices while still being energy and memory efficient. Therefore, substantial time and memory requirements are imposed on neural networks. One way to improve neural network efficiency is to simplify computations inside a neuron. A bipolar morphological neuron uses only addition, subtraction, and maximum operations inside the neuron and exponent and logarithm as activation functions for the network layers. These operations allow fast and compact gate implementation for FPGA and ASIC. In the paper, we consider the usage of bipolar morphological (BM) networks for document binarization. We examine the DIBCO 2017 binarization challenge and train the bipolar morphological convolutional neural network of U-Net architecture. Despite some accuracy decrease for a model with all BM convolutional layers, one can flexibly control the accuracy by using the partially converted model. It should be noted that even the fully BM model is suitable for solving the problem in practice.

Citation Download Citation

Elena Limonova, Dmitry Nikolaev, and Vladimir Arlazarov "Bipolar morphological U-Net for document binarization", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116050P (4 January 2021); https://doi.org/10.1117/12.2587174

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