# Task-Driven Real-World Super-Resolution of Document Scans
## 5 Conclusions and Future Work  
In this paper, we have presented a multi-task training framework for SR networks aimed at real-world text document scans. By incorporating text detection, recognition, and keypoint alignment components into the loss function, and balancing them relying on DWA, our model generates super-resolved images, which significantly improves the OCR-related text detection metric on challenging scans. Compared to conventional SR trained on simulated data, our approach better captures the semantics of text content and produces images more appropriate for further processing. Through our extensive experiments backed by statistical analysis, we demonstrated that it is critical to both exploit real-world training data and to incorporate task-specific components into the loss function. Compared with the baseline, the IoU text detection score was improved by 1.09% and 1.94% for simulated and real-world datasets, respectively. Although these gains are of a relatively small magnitude, they are statistically significant and they allow us to positively verify the hypothesis that including the task-oriented loss function components contributes to finding more valuable regions of the SR solution space. In the reported case, the task-related performance is limited by the very nature of SISR, which cannot introduce any new information beyond that present in the input image and learned from the training data. Importantly, the elaborated methodology can be further exploited to improve SR approaches underpinned with information fusion.  
Our ongoing research is aimed at exploiting the developed framework for training multi-image SR techniques, including our recent graph attention network [53], which we expect to allow for enhancing the OCR performance. Importantly, our dataset already contains multiple images per scene, and it can be exploited for the training and validation of multi-image fusion techniques. At the same time, we plan to elaborate validation procedures that will incorporate text recognition metrics in addition to text localization in document scans. This approach will enable the evaluation of SR techniques within an end-to-end character recognition pipeline, thereby bridging the gap to practical real-world applications.  
Conceptualization, M.K.; data curation, M.Z.; formal analysis, A.K. and M.K.; funding acquisition, M.K.; investigation, M.Z., T.T., A.K. and M.K.; methodology, A.K. and M.K.; project administration, M.K.; resources, M.K.; software, M.Z., T.T. and J.S.; supervision, M.K.; validation, M.Z. and T.T.; visualization, M.Z., A.K. and M.K.; writing--original draft, M.Z., A.K. and M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Centre, Poland, under Research Grant no. 2022/47/B/ST6/03009. M.K. was supported by the SUT funds through the Rector's Research and Development Grant 02/080/RGJ25/0053.  
Not applicable.  
The data exploited in this papers are available upon request.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.