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Metric Rectification of Curved Document Images

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5 Author(s)
Gaofeng Meng ; Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China ; Chunhong Pan ; Shiming Xiang ; Jiangyong Duan
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In this paper, we propose a metric rectification method to restore an image from a single camera-captured document image. The core idea is to construct an isometric image mesh by exploiting the geometry of page surface and camera. Our method uses a general cylindrical surface (GCS) to model the curved page shape. Under a few proper assumptions, the printed horizontal text lines are shown to be line convergent symmetric. This property is then used to constrain the estimation of various model parameters under perspective projection. We also introduce a paraperspective projection to approximate the nonlinear perspective projection. A set of close-form formulas is thus derived for the estimate of GCS directrix and document aspect ratio. Our method provides a straightforward framework for image metric rectification. It is insensitive to camera positions, viewing angles, and the shapes of document pages. To evaluate the proposed method, we implemented comprehensive experiments on both synthetic and real-captured images. The results demonstrate the efficiency of our method. We also carried out a comparative experiment on the public CBDAR2007 data set. The experimental results show that our method outperforms the state-of-the-art methods in terms of OCR accuracy and rectification errors.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:34 ,  Issue: 4 )

Date of Publication:

April 2012

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