52 research outputs found
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Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : μμ°κ³Όνλν μ리과νλΆ, 2022. 8. νλν.In recent days, most of the scanned images are obtained from mobile devices such as cameras, smartphones, and tablets rather than traditional flatbed scanners. Contrary to the scanning process of the traditional scanners, capturing process of mobile devices might be accompanied by distortions in various forms such as perspective distortion, fold distortion, and page curls. In this thesis, we propose robust dewarping methods which correct such distortions based on the document boundary and 3D reconstruction. In the first method, we construct a curvilinear grid on the document image using the document boundary and reconstruct the document surface in the three dimensional space. Then we rectify the image using a family of local homographies computed from the reconstructed document surface. Although some of the steps of the proposed method have been proposed separately in other research, our approach exploited and combined their advantages to propose a robust dewarping process in addition to improving the stability in the overall process. Moreover, we refined the process by correcting the distorted text region boundary and developed this process into an independent dewarping method which is concise, straight-forward, and robust while still producing a well-rectified document image.μ΅κ·Όμλ λλΆλΆμ μ€μΊλ μ΄λ―Έμ§λ€μ΄ μ ν΅μ μΈ ννμ€μΊλκ° μλ μΉ΄λ©λΌ, μ€λ§νΈν°, νλΈλ¦Ώ PC λ±μ ν΄λκΈ°κΈ°λ€λ‘λΆν° μ»μ΄μ§λ€. μ΄μ μ€μΊλλ€μ μ€μΊλ κ³Όμ κ³Όλ λ€λ₯΄κ² ν΄λκΈ°κΈ°λ€μ μ΄μ©ν μ΄λ―Έμ§ μΊ‘μ³λ§ κ³Όμ μ μκ·Όμ곑, μ’
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μ€νΈ μμμ κ²½κ³λ₯Ό μμ νμ¬ μ 체μ μΈ κ³Όμ μ 보μνμκ³ , μ΄ μ μ°¨λ₯Ό κ°κ²°νκ³ , μ§κ΄μ μ΄λ©°, κ°λ ₯νλ©΄μλ μ’μ κ²°κ³Όλ₯Ό λ΄λ λ
립μ μΈ λμν λ°©λ²μΌλ‘ κ°λ°νμλ€.1. Introduction 1
2. Review on Camera Geometry 6
2.1. Basic Camera Model 6
2.2. 3D Reconstruction Problem 8
3. Related Works 10
3.1. Dewarping Methods based on the Text-lines 10
3.2. Dewarping Methods based on the Document Boundary 11
3.3. Dewarping Methods based on the Grid Construction 12
3.4. Dewarping Methods based on the Document Surface Model in 3D Space 13
4. Document Image Dewarping based on the Document Boundary and 3D Reconstruction 15
4.1. Input Document Image Processing 17
4.1.1. Binarization of the Input Document Image 17
4.1.2. Perspective Distortion Removal using the Document Boundary 19
4.2. Grid Construction on the Document Image 21
4.3. 3D Reconstruction of the Document Surface 23
4.3.1. Geometric Model 23
4.3.2. Normalization of the Grid Corners 24
4.3.3. 3D Reconstruction of the Document Surface 26
4.4. Rectification of the Document Image under a Family of Local Homographies 27
4.5. Global Rectification of the Document Image 29
5. Document Image Dewarping by Straightening Document Boundary Curves 33
6. Conclusion 37
Appendix A. 38
A.1. 4-point Algorithm 38
A.2. Optimization of the Cost Function 40
Bibliography 42
Abstract (in Korean) 47
Acknowledgement (in Korean) 48μ
Recovering Homography from Camera Captured Documents using Convolutional Neural Networks
Removing perspective distortion from hand held camera captured document
images is one of the primitive tasks in document analysis, but unfortunately,
no such method exists that can reliably remove the perspective distortion from
document images automatically. In this paper, we propose a convolutional neural
network based method for recovering homography from hand-held camera captured
documents.
Our proposed method works independent of document's underlying content and is
trained end-to-end in a fully automatic way. Specifically, this paper makes
following three contributions: Firstly, we introduce a large scale synthetic
dataset for recovering homography from documents images captured under
different geometric and photometric transformations; secondly, we show that a
generic convolutional neural network based architecture can be successfully
used for regressing the corners positions of documents captured under wild
settings; thirdly, we show that L1 loss can be reliably used for corners
regression. Our proposed method gives state-of-the-art performance on the
tested datasets, and has potential to become an integral part of document
analysis pipeline.Comment: 10 pages, 8 figure
MataDoc: Margin and Text Aware Document Dewarping for Arbitrary Boundary
Document dewarping from a distorted camera-captured image is of great value
for OCR and document understanding. The document boundary plays an important
role which is more evident than the inner region in document dewarping. Current
learning-based methods mainly focus on complete boundary cases, leading to poor
document correction performance of documents with incomplete boundaries. In
contrast to these methods, this paper proposes MataDoc, the first method
focusing on arbitrary boundary document dewarping with margin and text aware
regularizations. Specifically, we design the margin regularization by
explicitly considering background consistency to enhance boundary perception.
Moreover, we introduce word position consistency to keep text lines straight in
rectified document images. To produce a comprehensive evaluation of MataDoc, we
propose a novel benchmark ArbDoc, mainly consisting of document images with
arbitrary boundaries in four typical scenarios. Extensive experiments confirm
the superiority of MataDoc with consideration for the incomplete boundary on
ArbDoc and also demonstrate the effectiveness of the proposed method on
DocUNet, DIR300, and WarpDoc datasets.Comment: 12 page
A Book Reader Design for Persons with Visual Impairment and Blindness
The objective of this dissertation is to provide a new design approach to a fully automated book reader for individuals with visual impairment and blindness that is portable and cost effective. This approach relies on the geometry of the design setup and provides the mathematical foundation for integrating, in a unique way, a 3-D space surface map from a low-resolution time of flight (ToF) device with a high-resolution image as means to enhance the reading accuracy of warped images due to the page curvature of bound books and other magazines. The merits of this low cost, but effective automated book reader design include: (1) a seamless registration process of the two imaging modalities so that the low resolution (160 x 120 pixels) height map, acquired by an Argos3D-P100 camera, accurately covers the entire book spread as captured by the high resolution image (3072 x 2304 pixels) of a Canon G6 Camera; (2) a mathematical framework for overcoming the difficulties associated with the curvature of open bound books, a process referred to as the dewarping of the book spread images, and (3) image correction performance comparison between uniform and full height map to determine which map provides the highest Optical Character Recognition (OCR) reading accuracy possible. The design concept could also be applied to address the challenging process of book digitization. This method is dependent on the geometry of the book reader setup for acquiring a 3-D map that yields high reading accuracy once appropriately fused with the high-resolution image. The experiments were performed on a dataset consisting of 200 pages with their corresponding computed and co-registered height maps, which are made available to the research community (cate-book3dmaps.fiu.edu). Improvements to the characters reading accuracy, due to the correction steps, were quantified and measured by introducing the corrected images to an OCR engine and tabulating the number of miss-recognized characters. Furthermore, the resilience of the book reader was tested by introducing a rotational misalignment to the book spreads and comparing the OCR accuracy to those obtained with the standard alignment. The standard alignment yielded an average reading accuracy of 95.55% with the uniform height map (i.e., the height values of the central row of the 3-D map are replicated to approximate all other rows), and 96.11% with the full height maps (i.e., each row has its own height values as obtained from the 3D camera). When the rotational misalignments were taken into account, the results obtained produced average accuracies of 90.63% and 94.75% for the same respective height maps, proving added resilience of the full height map method to potential misalignments
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λ리 μ νΉμ λ°μ½λ)μ ν¬ν¨νκ³ μλ€λ κ΄μΈ‘μ κ·Όκ±°νμ¬, μ μνλ λ°©λ²μ μμ μ μν μ λΆλ€μ λν ν¨μλ₯Ό μ΄μ©νμ¬ νμ΄μ§ νλ©΄μ ννννλ€. λ€μν λ₯κ·Ό 물체μ νμ΄μ§ νλ©΄ μμλ€μ λν μ€ν κ²°κ³Όλ€μ μ μνλ λ°©λ²μ΄ νννλ₯Ό μ ννκ² μνν¨μ 보μ¬μ€λ€.The optical character recognition (OCR) of text images captured by cameras plays an important role for scene understanding.
However, the OCR of camera-captured image is still considered a challenging problem, even after the text detection (localization).
It is mainly due to the geometric distortions caused by page curve and perspective view, therefore their rectification has been an essential pre-processing step for their recognition.
Thus, there have been many text image rectification methods which recover the fronto-parallel view image from a single distorted image.
Recently, many researchers have focused on the properties of the well-rectified text.
In this respect, this dissertation presents novel alignment properties for text image rectification, which are encoded into the proposed cost functions.
By minimizing the cost functions, the transformation parameters for rectification are obtained.
In detail, they are applied to three topics: document image dewarping, scene text rectification, and curved surface dewarping in real scene.
First, a document image dewarping method is proposed based on the alignments of text-lines and line segments.
Conventional text-line based document dewarping methods have problems when handling complex layout and/or very few text-lines. When there are few aligned text-lines in the image, this usually means that photos, graphics and/or tables take large portion of the input instead.
Hence, for the robust document dewarping, the proposed method uses line segments in the image in addition to the aligned text-lines.
Based on the assumption and observation that all the transformed line segments are still straight (line to line mapping), and many of them are horizontally or vertically aligned in the well-rectified images, the proposed method encodes this properties into the cost function in addition to the text-line based cost.
By minimizing the function, the proposed method can obtain transformation parameters for page curve, camera pose, and focal length, which are used for document image rectification. Considering that there are many outliers in line segment directions and miss-detected text-lines in some cases, the overall algorithm is designed in an iterative manner. At each step, the proposed method removes the text-lines and line segments that are not well aligned, and then minimizes the cost function with the updated information.
Experimental results show that the proposed method is robust to the variety of page layouts.
This dissertation also presents a method for scene text rectification. Conventional methods for scene text rectification mainly exploited the glyph property, which means that the characters in many language have horizontal/vertical strokes and also some symmetric shapes.
However, since they consider the only shape properties of individual character, without considering the alignments of characters, they work well for only images with a single character, and still yield mis-aligned results for images with multiple characters.
In order to alleviate this problem, the proposed method explicitly imposes alignment constraints on rectified results. To be precise, character alignments as well as glyph properties are encoded in the proposed cost function, and the transformation parameters are obtained by minimizing the function.
Also, in order to encode the alignments of characters into the cost function, the proposed method separates the text into individual characters using a projection profile method before optimizing the cost function. Then, top and bottom lines are estimated using a least squares line fitting with RANSAC. Overall algorithm is designed to perform character segmentation, line fitting, and rectification iteratively.
Since the cost function is non-convex and many variables are involved in the function, the proposed method also develops an optimization method using Augmented Lagrange Multiplier method.
This dissertation evaluates the proposed method on real and synthetic text images and experimental results show that the proposed method achieves higher OCR accuracy than the conventional approach and also yields visually pleasing results.
Finally, the proposed method can be extended to the curved surface dewarping in real scene.
In real scene, there are many circular objects such as medicine bottles or cans of drinking water, and their curved surfaces can be modeled as Generalized Cylindrical Surfaces (GCS). These curved surfaces include many significant text and figures, however their text has irregular structure compared to documents. Therefore, the conventional dewarping methods based on the properties of well-rectified text have problems in their rectification.
Based on the observation that many curved surfaces include well-aligned line segments (boundary lines of objects or barcode), the proposed method rectifies the curved surfaces by exploiting the proposed line segment terms.
Experimental results on a range of images with curved surfaces of circular objects show that the proposed method performs rectification robustly.1 Introduction 1
1.1 Document image dewarping 3
1.2 Scene text rectification 5
1.3 Curved surface dewarping in real scene 7
1.4 Contents 8
2 Related work 9
2.1 Document image dewarping 9
2.1.1 Dewarping methods using additional information 9
2.1.2 Text-line based dewarping methods 10
2.2 Scene text rectification 11
2.3 Curved surface dewarping in real scene 12
3 Document image dewarping 15
3.1 Proposed cost function 15
3.1.1 Parametric model of dewarping process 15
3.1.2 Cost function design 18
3.1.3 Line segment properties and cost function 19
3.2 Outlier removal and optimization 26
3.2.1 Jacobian matrix of the proposed cost function 27
3.3 Document region detection and dewarping 31
3.4 Experimental results 32
3.4.1 Experimental results on text-abundant document images 33
3.4.2 Experimental results on non conventional document images 34
3.5 Summary 47
4 Scene text rectification 49
4.1 Proposed cost function for rectification 49
4.1.1 Cost function design 49
4.1.2 Character alignment properties and alignment terms 51
4.2 Overall algorithm 54
4.2.1 Initialization 55
4.2.2 Character segmentation 56
4.2.3 Estimation of the alignment parameters 57
4.2.4 Cost function optimization for rectification 58
4.3 Experimental results 63
4.4 Summary 66
5 Curved surface dewarping in real scene 73
5.1 Proposed curved surface dewarping method 73
5.1.1 Pre-processing 73
5.1 Experimental results 74
5.2 Summary 76
6 Conclusions 83
Bibliography 85
Abstract (Korean) 93Docto
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