Self-supervised degraded document image binarization method based on second-order central moments and multi-scale network
Jinlu Zhang, Liyu Lin, Changlong Jin · 2021
In this work, we propose an effective self-supervised method for document image binarization. The proposed method is based on image second-order central moment and multi-scale convolution neural network (CNN). It effectively binarizes document images by addressing degradation issues (such as uneven illumination, ink stain, and fading). We first remove noticeable noise and performs data normalization in preprocessing step. Then the pseudo binarization image is generated by the second-order central moment algorithm. Then a multi-scale self-supervised network is utilized to distinguish the foreground (character) from the degraded image (background). We combine traditional image processing and selfsupervised networks to ensure the efficiency and effectiveness of the method while improving the generalization ability on multiple data sets. Extensive experiments show that the proposed model performs best in DIBCO benchmarks.