# Document Image Shadow Removal Based on Illumination Correction Method
## 2 Related Works
### Document Shadow Removal Dataset
#### 2.1.1 The Real Datasets  
The Adobe dataset [13] is the first real dataset for shadow removal with 81 document images with different shadow intensities and shapes. Each image has five to nine shading variations to simulate different lighting conditions. Adobe contains eleven document images, each of which is subjected to varying degrees of shading, resulting in multiple shading conditions. This setup allows the dataset to be effectively used to quantitatively and qualitatively evaluate the performance of shadow removal techniques.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Number of Images** &
\begin{tabular}{c} **Shadow Mask** \\ **Is Available** \\ \end{tabular} & **Real/Synthetic** \\ \hline Adobe [13] & 81 & no & real \\ Jung [14] & 87 & no & real \\ Kligler [15] & 381 & no & real \\ OSR [16] & 237 & yes & real \\ HS [17] & 100 & no & real \\ FSDARD [18] & 14,200 & yes & synthetic \\ SDCSRD [19] & 17,624 & yes & synthetic \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of document shadow removal datasets.  
The Jung dataset [14] is mainly composed of two types of images: those taken with a smartphone camera and those with a scanner. These digital document images are generated in different lighting conditions to simulate the various circumstances when taking pictures in the real world. Jung contains a series of real shadow-free images and corresponding shaded document images. It provides a practical test basis for evaluating document shadow removal algorithms, especially considering image quality differences between smartphones and scanners.  
The Kligler dataset [15] consists of 381 pairs of corresponding shaded and unshaded document images, divided into four categories: handwritten documents, printed documents, posters, and fonts. Each category consists of 7-10 documents, and each document has 8-12 different variations in shadow intensity, shape, and position. Kligler also includes some images with small characters, small color variations, and light shadows that are very similar to the shadow-free images. It can provide some challenging test conditions, including different types of text, levels of degradation, stains and creases, loss of text, etc.  
The OSR dataset [16] provides a diverse set of shadow images, divided into two parts: images created in a controlled lighting environment and those captured in a natural scene. The former contains 237 shadow images and their corresponding shadow-free images. These images are mainly taken from typical documents such as books, newspapers, and brochures. During the photo-taking process, the documents and camera were fixed on the table and holder, respectively, to maintain the photo angle. Shadow-free images are taken first; then, shadow images are taken using lights and occlusions (e.g., hands and pens). The document, table, and camera positions were fixed to ensure consistency and reliability. The other part contains 24 real images obtained from the Internet, which have different sizes and are used to model different lighting conditions and shadow intensities. OSR contains not only standardized images taken in a controlled environment but also real images taken in natural scenes, which ensures that the dataset has good diversity in shadow intensity, type, and distribution. The OSR dataset also gives the corresponding shadow mask for each shaded image and provides a valuable test basis for shadow removal research in document images, especially in strong shadow scenes.  
The HS dataset [17] contains 100 images covering 10 documents, with 10 images per document. To simulate multiple different scenarios, half of the images were captured under sunlight and the other half under LED lights, and different occlusion shapes were used to create shadows. HS provides sample images taken under more complex and variable lighting conditions, especially images containing hard shadows and shadow transition regions, making the testing of the shadow removal algorithm more challenging and practical.