###### Abstract  
In this paper, we propose a new convolutional neural network (CNN) architecture for improving document-image quality through decreasing the impact of distortions (i.e., blur, shadows, contrast issues, and noise) contained therein. Indeed, for many document-image processing systems such as OCR (optical character recognition) and document-image classification, the real-world image distortions can significantly degrade the performance of such systems in a way such that they become merely unusable. Therefore, a robust document-image enhancement model is required to preprocess the involved document images. The preprocessor system developed in this paper places \"deblurring\" and \"noise removal and contrast enhancement\" in two separate and sequential submodules. In the architecture of those two submodules, three new parts are introduced: (a) the patch-based approach, (b) preprocessing layer involving Gabor and Blur filters, and (c) the approach using residual blocks. Using these last-listed innovations results in a very promising performance when compared to the related works. Indeed, it is demonstrated that even extremely strongly degraded document images that were not previously recognizable by an OCR system can now become well-recognized with a 91.51% character recognition accuracy after the image enhancement preprocessing through our new CNN model. convolutional neural network; blind image enhancement; blur enhancement; contrast enhancement; noise reduction Article  
Deep Neural Network Concept for a Blind Enhancement of Document-Images in the Presence of Multiple Distortions  
[PERSON], [PERSON]. Image enhancement is widely used in atmospheric sciences [8], astrophotography [9], medical image processing [10], satellite image analysis, texture synthesis, remote sensing [11], digital photography, surveillance, and various video processing applications [12; 13]. Image enhancement can be explained by using the following formula (see Equation (1)):  
\[S=T(D) \tag{1}\]  
where \(S\) is the target image, \(D\) is the degraded input image, and \(T\) is a nonlinear function, transforming the degraded input image into its related \"original\" non-distorted image. For finding the function \(T\), we can define a function \(Z\) in such a way that by minimizing it (i.e., by \(Z\)), the function \(T\) will be converging to the required image enhancement function given in Equation (1). Thus, our target function \(Z\) is defined by using the following formula (see Equation (2)):  
\[\text{min}Z=\left\|S_{e}-T(D)\right\|_{2} \tag{2}\]  
where the \(S_{e}\) is the expected or assumed original \"clean \"or non-distorted image. On the other hand, it is well-known that the edges of or in character images are very important for a good performance on the OCR character recognition apps [14]. Therefore, we can add a so-called Sobel pre-filter to both sides of Equation (1). This results in the  
\[Sobel(S)=Sobel(T(D)) \tag{3}\]  
By adding Equation (3) to our target function (see Equation (2)), we obtain the following new formula (see Equation (4)):  
\[\text{min}Z=\left\|S_{e}-T(D)\right\|_{2}+\left\|Sobel(S_{e})-Sobel(T(D)) \right\|_{2} \tag{4}\]  
where \(T\) is our target nonlinear function and \(S_{e}\) is our expected original image. In Equation (4), we try to estimate the best-possible nonlinear function \(T\) by minimizing our target function \(Z\) described in Equation (4). This (i.e., Equation (4)) would be our loss function (while designing and training an appropriate deep convolutional neural network), which is used for estimating the nonlinear function \(T\). For estimating the function \(T\), two different approaches exist, traditional methods and deep neural network concepts. Regarding the traditional methods, they essentially solve the problem via analytical and heuristic schemes [15; 16]. In the second approach, such as the method proposed in this paper, convolutional neural network (CNN) models are used.