# A Review of Document Image Enhancement Based on Document Degradation Problem
## 3 Document Image Degradation Methods
### Handwriting Fading Problem  
In digitizing many historical books, the document images have a degraded condition of faded handwriting. A series of models have been proposed for this situation in image processing. In 2018, [PERSON] et al. proposed an adaptive global threshold selection algorithm based on structural symmetric pixels (SSP) [34]. First, they proposed an adaptive global threshold selection algorithm for SSP extraction, which can remove bleeding backgrounds while preserving fuzzy characters. SSP extraction enables feature maps of arbitrary size to output a vector of fixed length. Then, the proposed stroke width estimation algorithm improves adaptiveness. Finally, an iterative algorithm is used to estimate the text stroke width and remove the noise at the same time. This method is more suitable for processing printed text images than handwritten text images. In 2019, [PERSON]. [35] introduced an adversarial learning approach, as shown in Figure 6. This method constructs a Texture Augmentation Network that transfers the texture element of a degraded reference document image to a clean binary image. The available document dataset is expanded by adding various noisy textures to the same textual content to form multiple versions of the degraded image. Finally, the newly generated images are passed through the network in order to return clean versions. The model is effective in improving robustness, and it does not require paired data. The model is similar to the cycleGAN approach, but the results obtained are more advantageous, thanks to the ability of BiNet in the model to learn autonomously and to fully supervise the optimization results. In 2021, [PERSON] et al. proposed an algorithm based on background estimation and energy minimization [36]. First, a disc-shaped structuring element whose radius is computed by the minimum entropy-based stroke width transform (SWT) is used to estimate and compensate for the document background. SWT can extract the stroke width to obtain the character candidate region and filter out the mis-checked candidates using a priori knowledge of shape and texture. Then, Laplacian energy-based segmentation is performed on the compensated document images. Finally, post-processing is implemented to preserve text stroke connectivity and eliminate isolated noise. This method is able to better preserve text strokes, but it does not have more advantages than the current advanced deep-learning-based methods. In the same year, the DP-LinkNet model [37] was proposed by [PERSON] et al. The model follows the LinkNet [38] and D-LinkNet [39] architectures with the addition of the hybrid dilated convolution (HDC) module and the spatial pyramid pooling (SPP) module, which expands the perceptual field and aggregates multiscale contextual features, and an SPP module that encodes the output of the HDC with multicore pooling, both of which enable the model to extract textual strokes features with deep semantic information. Compared to other neural network models, training is faster and does not require deeper network layers. In 2022, [PERSON] et al. proposed a two-stage color document image enhancement and binarization method using generative adversarial neural networks [40]. The first stage uses a color-independent adversarial neural network to remove background information from local image blocks and extract colored foreground information for document image enhancement. The second stage uses a multiscale adversarial neural network to generate a local binarized resultant image and a global binarized resultant image of the document image. One of the model's advantages is its ability to handle multi-color degradation of document images. The disadvantage is that the training is slow due to the need to generate images of different channels. In the same year, [PERSON] et al.