## 5 Conclusions  
In this paper, we developed a new deep neural model to reliably and memory-efficiently deblur, denoise, and enhance the contrast of document images so that they become more readable for OCR software systems. This accuracy is achieved by sequentially combining two different enhancement modules, the first for deblurring and the second for jointly denoising and contrast enhancement. Each of these two named modules were appropriately trained with an appropriate specialized training set for the respective specific sub-tasks. This task separation shows an outstanding performance. Our Module 1 model performs excellently in the blur part and outperforms other deblurring methods by generating the most recognizable text image. In the noise and contrast enhancement part, Module 2, the developed model shows a superb denoising effect compared to the previous salt and pepper denoising methods. It outperforms the best of those related models in the range of 5% to 8%. Our contrast is not comparable, as text contrast enhancement has a different meaning than other contrast enhancement types. After enhancement, our global model's output images are significantly more readable by an OCR system. This enhanced readability is reached even in cases of very strongly blur-distorted document images; we could see an accuracy improving from 13.56% to about 91.51%. A core limitation of the concept developed in this paper is that it is valid and has been validated so far only for document images and not for other types of images. Document images are different and specific compared to other more general image types. Thus, the specific image enhancement endeavor in the context of this paper has the sole purpose of making document images more readable by OCR systems. This is a very specific context and specification, which is not the same for all other types of images. For example, if a distorted marriage photo needs to be enhanced, the enhancement target does not necessarily match the ones of a document image. Indeed, an enhanced document image may still contain some form of dirt, especially in the background. However, the readability through an OCR of the text contained therein was significantly improved. Therefore, possible future works of interest may relate to developing a novel global neural model which shall be optimized to various types of images as needed in various application contexts (examples of contexts: smartphone cameras, scene analysis or a self-driving car, video streaming through a smartphone, etc. ). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Considered Image**} & \multicolumn{5}{c}{**CRA in Different Image Quality**} \\  & \multicolumn{5}{c}{**(By the Tesseract OCR)**} \\ \cline{2-6}  & **Very Good** & **Good** & **Middle** & **Bad** & **Very Bad** \\ \hline _Mix distorted (blur + noise + contrast)_ & 95.23 & 85.32 & 57.21 & 33.26 & 13.56 \\ _reference images_ & 94.78 & 85.48 & 66.71 & 56.12 & 46.88 \\ \cline{2-6} _[PERSON] et al._[39] & 93.10 & 86.34 & 67.23 & 57.36 & 57.64 \\ \cline{2-6} _[PERSON] et al._[16] + NLSF CNN_[60] & 95.40 & 87.83 & 82.53 & 80.73 & 77.53 \\ \cline{2-6} _[PERSON] et al._[39] + NLSF CNN_[60] & 95.35 & 88.25 & 84.45 & 81.65 & 75.45 \\ \cline{2-6} _Our global model_ & 97.12 & 98.52 & 95.15 & 92.13 & 91.51 \\ \cline{2-6} _(Module 1 + Model 2, see Figure 3)_ & 97.12 & 98.52 & 95.15 & 92.13 & 91.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The character recognition accuracy (CAR) was obtained for all enhanced images using our own dataset after testing the Tesseract OCR software [73] version 5.0.0. For each test, 100 images are used for testing each model considered here from our own dataset of real-world document images. **Author Contributions:** Conceptualization, K.M., V.T. and K.K. ; Methodology, K.K. ; Software, K.M. and V.T. ; Validation, K.M., V.T. and K.K. ; Formal Analysis, K.M. ; Investigation, K.M. and V.T. ; Resources, K.M. ; Data Curation, K.M. ; Writing--Original Draft Preparation, K.M. and V.T. ; Writing--Review and Editing, K.M., V.T.