Unfolder: Fast localization and image rectification of a document with a crease from folding in half
A.M. Ershov, D.V. Tropin, E.E. Limonova, D.P. Nikolaev, V.V., Arlazarov

TL;DR
Unfolder is a fast, robust algorithm specifically designed for rectifying images of folded documents, outperforming neural network methods in accuracy and speed, and is suitable for mobile device deployment.
Contribution
The paper introduces Unfolder, a novel rectification method tailored for folded documents, with a new dataset and superior accuracy and efficiency over existing neural network approaches.
Findings
Unfolder achieves a recognition error rate of 0.33.
Unfolder runs in only 0.25 seconds per image on a smartphone.
Unfolder outperforms DocTr and DewarpNet in accuracy.
Abstract
Presentation of folded documents is not an uncommon case in modern society. Digitizing such documents by capturing them with a smartphone camera can be tricky since a crease can divide the document contents into separate planes. To unfold the document, one could hold the edges potentially obscuring it in a captured image. While there are many geometrical rectification methods, they were usually developed for arbitrary bends and folds. We consider such algorithms and propose a novel approach Unfolder developed specifically for images of documents with a crease from folding in half. Unfolder is robust to projective distortions of the document image and does not fragment the image in the vicinity of a crease after rectification. A new Folded Document Images dataset was created to investigate the rectification accuracy of folded (2, 3, 4, and 8 folds) documents. The dataset includes 1600…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Industrial Vision Systems and Defect Detection · Advanced Image and Video Retrieval Techniques
