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Adaptive Dewarping of Severely Warped Camera-captured Document Images Based on Document Map Generation by C. H. Nachappa; N. Shobha Rani; Peeta Basa Pati; M. Gokulnath is a Computer Science article available to read on EtoBox.

What is Adaptive Dewarping of Severely Warped Camera-captured Document Images Based on Document Map Generation about?

Automated dewarping of camera-captured handwritten documents is a challenging research problem in Computer Vision and Pattern Recognition. Most available systems assume the shape of the camera-captured image boundaries to be anywhere between trapezoidal and octahedral, with linear distortion in areas between the boundaries for dewarping. The majority of the state-of-the-art applications successfully dewarp the simple-to-medium range geometrical distortions with partial selection of control points by a user. The proposed work implements a fully automated technique for control point detection from simpleto-complex geometrical distortions in camera-captured document images. The input image is subject to preprocessing, corner point detection, document map generation, and rendering of the de-warped document image. The proposed algorithm has been tested on five different camera-captured document datasets (one internal and four external publicly available) consisting of 958 images. Both quantitative and qualitative evaluations have been performed to test the efficacy of the proposed system. On the quantitative front, an Intersection Over Union (IoU) score of 0.92, 0.88, and 0.80 for docum

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Author

C. H. Nachappa; N. Shobha Rani; Peeta Basa Pati; M. Gokulnath

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)