title: An improved physically-based method for geometric restoration of distorted document images.
abstract: In document digitization through camera-based systems, simple imaging setups often produce geometric distortions in the resultant 2D images because of the non-planar geometric shapes of certain documents such as thick bound books, rolled, folded or crumpled materials, etc. Previous works have demonstrated that arbitrary warped documents can be successfully restored by flattening a 3D scan of the document. These approaches use physically-based or relaxation-based techniques in their flattening process. While this has been demonstrated to be effective in rectifying the image content and improving OCR, these previous approaches have several limitations in terms of speed and stability. In this paper, we propose a distance-based penalty metric to replace the mass-spring model and introduce additional bending resistance and drag forces to improve the efficiency of the existing approaches. The use of Verlet integration and special plane collision handling schemes also help to achieve better stability without sacrificing efficiency. Experiments on various document images captured from books, brochures and historical documents with arbitrary warpings have demonstrated large improvements over the existing approaches in terms of stability and efficiency.
authors.affiliation: School of Computing, National University of Singapore, 3 Science Drive #2, Singapore. zhangli@comp.nus.edu.sg
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.forename: Chew
authors.initials: C
authors.lastname: Tan
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D004282:Documentation / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity