Effective geometric restoration of distorted historical documents for large-scale digitization

Yang, P; Antonacopoulos, A; Clausner, C; Pletschacher, S; Qi, J

Abstract

Due to storage conditions and material’s non-planar shape, geometric distortion of the 2-D content is widely present in scanned document images. Effective geometric restoration of these distorted document images considerably increases character recognition rate in large-scale digitisation. For large-scale digitisation of historical books, geometric restoration solutions expect to be accurate, generic, robust, unsupervised and reversible. However, most methods in the literature concentrate on improving restoration accuracy for specific distortion effect, but not their applicability in large-scale digitisation. This paper proposes an effective mesh based geometric restoration system, (GRLSD), for large-scale distorted historical document digitisation. In this system, an automatic mesh generation based dewarping tool is proposed to geometrically model and correct arbitrary warping historical documents. An XML based mesh recorder is proposed to record the mesh of distortion information for reversible use. A graphic user interface toolkit is designed to visually display and manually manipulate the mesh for improving geometric restoration accuracy. Experimental results show that the proposed automatic dewarping approach efficiently corrects arbitrarily warped historical documents, with an improved performance over several state-of-the-art geometric restoration methods. By using XML mesh recorder and GUI toolkit, the GRLSD system greatly aids users to flexibly monitor and correct ambiguous points of mesh for the prevention of damaging historical document images without distortions in large-scale digitalisation.

Citation

Yang, P., Antonacopoulos, A., Clausner, C., Pletschacher, S., & Qi, J. (2017). Effective geometric restoration of distorted historical documents for large-scale digitization. IET Image Processing, 11(10), 841-853. https://doi.org/10.1049/iet-ipr.2016.0973

• Journal Article Type: Article
• Acceptance Date: Mar 19, 2017
• Publication Date: Mar 23, 2017
• Deposit Date: May 4, 2017
• Publicly Available Date: May 4, 2017
• Journal: IET Image Processing
• Print ISSN: 1751-9659
• Electronic ISSN: 1751-9667
• Publisher: Institution of Engineering and Technology (IET)
• Volume: 11
• Issue: 10
• Pages: 841-853
• DOI: https://doi.org/10.1049/iet-ipr.2016.0973
• Publisher URL: http://dx.doi.org/10.1049/iet-ipr.2016.0973
• Related Public URLs: http://digital-library.theiet.org/content/journals/iet-ipr