title: Geometric rectification of camera-captured document images.
abstract: Compared to typical scanners, handheld cameras offer convenient, flexible, portable, and non-contact image capture, which enables many new applications and breathes new life into existing ones. However, camera-captured documents may suffer from distortions caused by non-planar document shape and perspective projection, which lead to failure of current OCR technologies. We present a geometric rectification framework for restoring the frontal-flat view of a document from a single camera-captured image. Our approach estimates 3D document shape from texture flow information obtained directly from the image without requiring additional 3D/metric data or prior camera calibration. Our framework provides a unified solution for both planar and curved documents and can be applied in many, especially mobile, camera-based document analysis applications. Experiments show that our method produces results that are significantly more OCR compatible than the original images.
authors.affiliation: Amazon.com, 701 5th Avenue #614.B, Seattle, WA 98104, USA. jliang@amazon.com
authors.forename: Jian
authors.initials: J
authors.lastname: Liang
authors.forename: Daniel
authors.initials: D
authors.lastname: DeMenthon
authors.forename: David
authors.initials: D
authors.lastname: Doermann
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*; D010781:Photography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity