# Voting-Based Document Image Skew Detection
## 5 Conclusions  
The algorithms backed up for each other in failure cases, resulting in almost 100% uptime. The failure cases were, in fact, splash pages containing images, with almost no visual orientation information, such as lines aligned with the document's primary axes.  
The Best First voting method turned out to give the best results.  
The average uptime for this voting policy is 99.5%, as only two images, which arguably do not contain any orientation information, were not deskewed correctly--both images contain organic paintings.  
Having many algorithms attempting to solve the same problem, each performing on a different aspect of the problem, while being able to estimate a confidence measure of their work, is an optimal case for running all algorithms and returning a result determined by vote, which is closer to the ground truth than any of the candidate algorithms.  
By fine-tuning the confidences of the candidate algorithms, we obtained a better uptime and overall accuracy.  
The technology presented in this paper was developed and employed in the \"Lib2 Life- Revitalizing Libraries and Cultural Heritage through Advanced Technologies\" project [31] aimed at both researching document image techniques suitable for automatic content creation and building of a large repository consisting from millions of pages of books, newspapers and journals with diverse content and spread across multiple time periods. Due to the large variations and unpredictability in the processing input, a strong need for algorithms that do not require user supervision emerged.  
The voting-based deskew method was validated on a very demanding dataset, encompassing all kinds of complex layouts, tables, alignments and illustrations. Moreover, it was successfully used until now in the first part of the document image processing in the aforementioned project, on a little more than half a million pages and the results were visually examined by dense sampling. Very encouragingly, no erroneous rotations of the pages were reported, thus one can easily conclude that the proposed approach is indeed a very robust one, ideally suited for the mass production of digital content.  
The source code and the benchmarking dataset for this project may be found at the following link: [[https://gitlab.com/adix64/document-deskewer](https://gitlab.com/adix64/document-deskewer)]([https://gitlab.com/adix64/document-deskewer](https://gitlab.com/adix64/document-deskewer)).  
Conceptualization, C.-A.B.; Data curation, O.-A.D.; Formal analysis, O.-A.D., C.P. ([PERSON]), N.C. and C.P. ([PERSON]); Funding acquisition, C.-A.B., C.P. ([PERSON]), N.C. and C.P. ([PERSON]); Methodology, C.-A.B.; Project administration, O.-A.D.; Resources, C.P. ([PERSON]), N.C. and C.P. ([PERSON]); Software, O.-A.D.; Supervision, C.-A.B.; Validation, C.-A.B.; Visualization, C.-A.B. and O.-A.D.; Writing--original draft, C.-A.B.; Writing--review & editing, C.-A.B., O.-A.D., C.P. ([PERSON]), N.C. and C.P. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Romanian Ministry of Research and Innovation, CCCDI--UEFiSCDI, project number PN-III-P1-1-2-PCCDI-2017-0689/,Lib2 Life- Revitalizarea bibliotecilor si a partitionuit cultural print technologi avansate/,Revalidizing Libraries and Cultural Heritage through Advanced Technologies\", within PNCDI III.  
The authors declare no conflict of interest.