Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks

The efficient segmentation of foreground text information from the background in degraded color document images is a hot research topic. Due to the imperfect preservation of ancient documents over a long period of time, various types of degradation, including staining, yellowing, and ink seepage, have seriously affected the results of image binarization. In this paper, a three-stage method is proposed for image enhancement and binarization of degraded color document images by using discrete wavelet transform (DWT) and generative adversarial network (GAN). In Stage-1, we use DWT and retain the LL subband images to achieve the image enhancement. In Stage-2, the original input image is split into four (Red, Green, Blue and Gray) single-channel images, each of which trains the independent adversarial networks. The trained adversarial network models are used to extract the color foreground information from the images. In Stage-3, in order to combine global and local features, the output image from Stage-2 and the original input image are used to train the independent adversarial networks for document binarization. The experimental results demonstrate that our proposed method outperforms many classical and state-of-the-art (SOTA) methods on the Document Image Binarization Contest (DIBCO) dataset. We release our implementation code at https://github.com/abcpp12383/ThreeStageBinarization

An Adaptive Test Sheet Generation Mechanism Using Genetic Algorithm

For test-sheet composition systems, it is important to adaptively compose test sheets with diverse conceptual scopes, discrimination and difficulty degrees to meet various assessment requirements during real learning situations. Computation time and item exposure rate also influence performance and item bank security. Therefore, this study proposes an Adaptive Test Sheet Generation (ATSG) mechanism, where a Candidate Item Selection Strategy adaptively determines candidate test items and conceptual granularities according to desired conceptual scopes, and an Aggregate Objective Function applies Genetic Algorithm (GA) to figure out the approximate solution of mixed integer programming problem for the test-sheet composition. Experimental results show that the ATSG mechanism can efficiently, precisely generate test sheets to meet the various assessment requirements than existing ones. Furthermore, according to experimental finding, Fractal Time Series approach can be applied to analyze the self-similarity characteristics of GA’s fitness scores for improving the quality of the test-sheet composition in the near future

The contrast in suspended particle dynamics at surface and near bottom on the river-dominated northern South China Sea shelf in summer: implication on physics and biogeochemistry coupling

To understand the process-response relations among physical forcing and biogeochemical properties of suspended particles (SPs) in the river-dominated northern South China Sea shelf, a 5-day shipboard observation was conducted at a fixed location on the dispersal pathway of the Zhujiang (Pearl) River plume (ZRP) in the summer of 2016. Instrumented moorings were deployed near the sampling site to record the flow and wave fields every 10 minutes. Hydrographic properties were measured hourly to identify different water masses. Water and SPs samples at the surface (3 m) and near the bottom (3 m above the bed) were taken every 3 h for the analyses of nutrients, chlorophyll-a (Chl-a), and particulate organic matter (POM including POC, PN, and δ13CPOC). Meanwhile, the grain-size composition of SPs and seafloor sediment were also analyzed. Results showed that monsoon winds drove cold upwelling and ZRP waters at the surface. Both the upwelling and ZRP regimes contained newly produced marine phytoplankton based on low POC/Chl-a ratio (PC ratio) and enriched δ13CPOC. However, SPs in the ZRP regime were smaller (<153 µm), having denser particle bulk density, and less enriched δ13CPOC, indicating different bio-communities from the upwelling regime. EOF analysis of the surface data suggested that mixing processes and the dispersal of the ZRP regime were mainly controlled by far-field storm winds, tidal modulation, and strength of mixing. On the other hand, a bottom nepheloid layer (BNL) was observed, mainly consisting of SPs<63 μm with higher bulk density than SPs at the surface. POM in the BNL was degraded and δ13CPOC-depleted according to the PC ratio and δ13CPOC. EOF analysis of the near-bottom data indicated that the dominant physical processes influencing the biogeochemical properties of SPs in the BNL were jointly the upwelling-associated lateral transport (first order) and tide-related resuspension (second order). Our study identified the contrast between the surface and near-bottom regimes with the coupling patterns among physical forcing and physiochemical properties of SPs using good constraints on particle dynamics and particle sources

CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization

To efficiently extract the textual information from color degraded document images is an important research topic. Long-term imperfect preservation of ancient documents has led to various types of degradation such as page staining, paper yellowing, and ink bleeding; these degradations badly impact the image processing for information extraction. In this paper, we present CCDWT-GAN, a generative adversarial network (GAN) that utilizes the discrete wavelet transform (DWT) on RGB (red, green, blue) channel splited images. The proposed method comprises three stages: image preprocessing, image enhancement, and image binarization. This work conducts comparative experiments in the image preprocessing stage to determine the optimal selection of DWT with normalization. Additionally, we perform an ablation study on the results of the image enhancement stage and the image binarization stage to validate their positive effect on the model performance. This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets, and outperforms other SOTA methods