# A Review of Document Image Enhancement Based on Document Degradation Problem
## 1 Introduction  
Document image enhancement is one of the pre-processing steps for document analysis and recognition. The document images are severely damaged or degraded due to long storage time, poor storage environment, and problems such as page smudges and fading of handwriting [1, 2].Or, during the digitization process, the degradation of document images is caused by poor lighting conditions, shadows, camera distortion noise, blur, etc. [3, 4, 5]. Degraded document images have low visual quality and legibility. They could contain handwritten or machine-printed text or a mixture of both. In addition, they could contain multiple handwriting styles and different languages. To solve this problem, many document image enhancement methods have been proposed. For example, the quality of the image is improved by removing the degradation effect present in the image to restore its original content [6, 7]. However, relying only on traditional methods, the task results for these degraded documents still need to be more significant, and it is challenging to automate document analysis. To achieve good results, it is necessary to manually enhance these images, and when the amount of data is relatively large, file image analysis becomes more difficult. Deep learning has been proposed and applied to different computer vision and image processing tasks, such as object detection [8, 9], semantic segmentation [10], face detection and dataset creation [11, 12, 13], image enhancement [14, 15, 16], etc. The data show that compared with traditional methods, methods based on deep learning achieve more gratifying results. Similarly, the method of dealing with document image enhancement has also received great attention. Thus, it is essential to develop methods that can automaticallyenhance the visual quality and legibility of these images. The purpose of this survey is to review current methods for degraded document image enhancement and to analyze and discuss the advantages and disadvantages of the leading models for various degradation tasks, focusing on the limitations of the model processing effects and then discussing the directions for future research.  
We have structured the rest of the paper as follows. Section 1 summarizes six degradation problems of documents, illustrated in Figure 1. In Section 2, we summarize the models to solve the corresponding problem and review the related work. In Section 3, we present the common public datasets and metrics and discuss the current limitations and challenges of each degradation task. Section 4 summarizes the current data problems in the field of document image enhancement and gives an outlook on the future of the document image enhancement field.  
We make several vital contributions in this paper: one is reviewing the research progress of deep-learning-based document image enhancement methods, especially in the past five years, to help readers better understand the current development in this field. On the other hand, we summarize six image degradation problems, namely, background texture, page smudge, word fading, uneven lighting, watermark, and blur, and analyze the characteristics, advantages, and disadvantages of the leading models to help researchers choose the right approach according to their needs.