Document image dewarping using robust estimation of curled text lines

Ulges, Adrian; Lampert, Christoph H.; Breuel, Thomas M.

doi:10.1109/icdar.2005.90

Cited by 98 publications

(44 citation statements)

References 5 publications

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“…Ulges et al [17] estimate quadrilateral cell for each letter based on local baselines finding and then map to a rectangle of corrected size and position in the dewarped image. Their method is not generic since it is based on the assumption that the original page contains only straight lines that are approximately equally spaced and sized while spacing between words is not large.…”

Section: B Rectification Techniques Based On 2-d Document Image Procmentioning

confidence: 99%

Goal-Oriented Rectification of Camera-Based Document Images

Stamatopoulos

Gatos

Pratikakis

et al. 2011

IEEE Trans. on Image Process.

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Abstract-Document digitization with either flatbed scanners or camera-based systems results in document images which often suffer from warping and perspective distortions that deteriorate the performance of current OCR approaches. In this paper, we present a goal-oriented rectification methodology to compensate for undesirable document image distortions aiming to improve the OCR result. Our approach relies upon a coarse-to-fine strategy. First, a coarse rectification is accomplished with the aid of a computationally low cost transformation which addresses the projection of a curved surface to a 2-D rectangular area. The projection of the curved surface on the plane is guided only by the textual content's appearance in the document image while incorporating a transformation which does not depend on specific model primitives or camera setup parameters. Second, pose normalization is applied on the word level aiming to restore all the local distortions of the document image. Experimental results on various document images with a variety of distortions demonstrate the robustness and effectiveness of the proposed rectification methodology using a consistent evaluation methodology that encounters OCR accuracy and a newly introduced measure using a semi-automatic procedure.Index Terms-Document image analysis, document image processing, document image rectification, image dewarping.

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Section: B Rectification Techniques Based On 2-d Document Image Procmentioning

confidence: 99%

Goal-Oriented Rectification of Camera-Based Document Images

Stamatopoulos

Gatos

Pratikakis

et al. 2011

IEEE Trans. on Image Process.

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“…In addition, traditional methods based on morphological operations and projection methods are extremely slow and tends to fail for camera-captured images. In this work, we choose a more robust approach based on Branch-and-Bound text line finding algorithm (RAST algorithm) [24] for skew detection and auto-rotation. The basic idea of this algorithm is to identify each line independently and use the slope of the best scoring line as the skew angle for the entire text segment.…”

Section: Text Skewmentioning

confidence: 99%

OCRdroid: A Framework to Digitize Text Using Mobile Phones

Zhang

Joshi²,

Kadmawala³

et al. 2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. As demand grows for mobile phone applications, research in optical character recognition, a technology well developed for scanned documents, is shifting focus to the recognition of text embedded in digital photographs. In this paper, we present OCRdroid, a generic framework for developing OCR-based applications on mobile phones. OCRdroid combines a light-weight image preprocessing suite installed inside the mobile phone and an OCR engine connected to a backend server. We demonstrate the power and functionality of this framework by implementing two applications called PocketPal and PocketReader based on OCRdroid on HTC Android G1 mobile phone. Initial evaluations of these pilot experiments demonstrate the potential of using OCRdroid framework for realworld OCR-based mobile applications.

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“…Previous approaches of curled textline detection [1,2,3,4,5,6,7,8] work on binarized images. These approaches can be divided into two categories: (a) heuristic search [1,2,3,4,5,6] and (b) active contours (snakes) [7,8].…”

Section: Introductionmentioning

confidence: 99%

Textline information extraction from grayscale camera-captured document images

Bukhari

Breuel

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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Cameras offer flexible document imaging, but with uneven shading and non-planar page shape. Therefore cameracaptured documents need to go through dewarping before being processed by traditional text recognition methods. Curled textline detection is an important step of dewarping. Previous approaches of curled textline detection use binarization as a pre-processing step, which can negatively affect the detection results under uneven shading. Furthermore, these approaches are sensitive to high degrees of curl and estimate x-line 1 and baseline pairs using regression which may result in inaccurate estimation. We introduce a novel curled textline detection approach for grayscale document images. First, the textline structure is enhanced by using match filter bank smoothing and then central lines of textlines are detected using ridges. Then, x-line and baseline pairs are estimated by adapting active contours (snakes) over ridges. Unlike other approaches, our approach does not use binarization and applies directly on grayscale images. We achieved 91% of detection accuracy with good estimation of x-line and baseline pairs on the dataset of CBDAR 2007 document image dewarping contest.

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Document image dewarping using robust estimation of curled text lines

Cited by 98 publications

References 5 publications

Goal-Oriented Rectification of Camera-Based Document Images

Goal-Oriented Rectification of Camera-Based Document Images

OCRdroid: A Framework to Digitize Text Using Mobile Phones

Textline information extraction from grayscale camera-captured document images

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