# A Review of Document Image Enhancement Based on Document Degradation Problem
## 3 Document Image Degradation Methods
### Problems and Development Direction  
Document image enhancement tasks are far from being solved; some tasks have only been studied minimally. In Table 5, we discuss these issues and future work.  
\begin{table}
\begin{tabular}{p{34.1 pt} p{34.1 pt}} \hline \hline
**Degradation** & \multicolumn{2}{c}{**Problems or Future Developments**} \\ \hline background texture & The challenge of the background texture problem is mostly for color document images, and much of the current research is still focused on the processing of grayscale text images with better results. The color document images, on the other hand, cause a loss of text content when processed by model algorithms due to the difference in color. Therefore, methods for color document images need to be investigated. The text images include handwritten images and printed images, and there is still less discussion on the comparison of these two types of datasets, which can be processed and studied. \\ \hline page smudge & Character recognition is very challenging when the image document has ghosting problems (ink is visible on the other side of the text page, but the ink is not precisely from the other side, causing interference with the text on the target page) and when the RGB image has a variety of ink colors and intensities that vary from moment to moment. Current methods are less effective for low-contrast document image bleed-through problems. Therefore, we need to propose a model approach based on such problems to solve these problems. \\ \hline handwriting fading & Although the fading of handwriting will affect the readability of the document and the OCR effect, there are severe and minor fading cases, and minor fading is still relatively easy to identify. This type of degradation problem deals more with handwritten text images. For severely faded documents, the datasets of many studies are not published, and there is still a need to produce datasets for such problems. \\ \hline the poor lighting conditions & The problem of overexposure in uneven illumination conditions, i.e., the result of adding too much flash to devices such as cameras during digitization, has been less studied [54; 55]. There is no suitable dataset for this problem. Therefore, the exposure processing problem in image document enhancement still needs sufficient research in the future. \\ \hline watermark removal & There is less research on watermark removal for document images, mainly due to the differences in language and text, resulting in limited watermark removal techniques for document images. For example, English text, a combination of 26 letters, and Chinese characters are three-dimensional homogeneous characters, resulting in less effective information redundancy in Chinese characters. Additionally, most of the methods are now only applicable to black text, the watermark color of a single document. However, watermark removal involves the integrity of the original content and intellectual property issues, so we need to handle it carefully. \\ \hline deblurring & Models mainly generate blurred text datasets, but this is not enough to be used in real scenarios, which are diverse and many situations are not considered. The current number of blurred text datasets is still insufficient, especially since Chinese datasets are even more scarce. The insufficient coverage of scenarios in the dataset also leads to algorithmic models that do not take advantage of them. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Degradation status problems or future developments.