Effective Geometric Restoration of Distorted Historical Document for Large-Scale Digitization

Due to storage conditions and material’s non-planar shape, geometric distortion of the 2-D content is widely present in scanned document images. Effective geometric restoration of these distorted document images considerably increases character recognition rate in large-scale digitisation. For large-scale digitisation of historical books, geometric restoration solutions expect to be accurate, generic, robust, unsupervised and reversible. However, most methods in the literature concentrate on improving restoration accuracy for specific distortion effect, but not their applicability in large-scale digitisation. This paper proposes an effective mesh based geometric restoration system, (GRLSD), for large-scale distorted historical document digitisation. In this system, an automatic mesh generation based dewarping tool is proposed to geometrically model and correct arbitrary warping historical documents. An XML based mesh recorder is proposed to record the mesh of distortion information for reversible use. A graphic user interface toolkit is designed to visually display and manually manipulate the mesh for improving geometric restoration accuracy. Experimental results show that the proposed automatic dewarping approach efficiently corrects arbitrarily warped historical documents, with an improved performance over several state-of-the-art geometric restoration methods. By using XML mesh recorder and GUI toolkit, the GRLSD system greatly aids users to flexibly monitor and correct ambiguous points of mesh for the prevention of damaging historical document images without distortions in large-scale digitalisation

정렬 특성들 기반의 문서 및 장면 텍스트 영상 평활화 기법

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 조남익.카메라로 촬영한 텍스트 영상에 대해서, 광학 문자 인식(OCR)은 촬영된 장면을 분석하는데 있어서 매우 중요하다. 하지만 올바른 텍스트 영역 검출 후에도, 촬영한 영상에 대한 문자 인식은 여전히 어려운 문제로 여겨진다. 이는 종이의 구부러짐과 카메라 시점에 의한 기하학적인 왜곡 때문이고, 따라서 이러한 텍스트 영상에 대한 평활화는 문자 인식에 있어서 필수적인 전처리 과정으로 여겨진다. 이를 위한 왜곡된 촬영 영상을 정면 시점으로 복원하는 텍스트 영상 평활화 방법들은 활발히 연구되어지고 있다. 최근에는, 평활화가 잘 된 텍스트의 성질에 초점을 맞춘 연구들이 주로 진행되고 있다. 이러한 관점에서, 본 학위 논문은 텍스트 영상 평활화를 위하여 새로운 정렬 특성들을 다룬다. 이러한 정렬 특성들은 비용 함수로 설계되어지고, 비용 함수를 최소화하는 방법을 통해서 평활화에 사용되어지는 평활화 변수들이 구해진다. 본 학위 논문은 문서 영상 평활화, 장면 텍스트 평활화, 일반 배경 속의 휘어진 표면 평활화와 같이 3가지 세부 주제로 나눠진다. 첫 번째로, 본 학위 논문은 텍스트 라인들과 선분들의 정렬 특성에 기반의 문서 영상 평활화 방법을 제안한다. 기존의 텍스트 라인 기반의 문서 영상 평활화 방법들의 경우, 문서가 복잡한 레이아웃 형태이거나 적은 수의 텍스트 라인을 포함하고 있을 때 문제가 발생한다. 이는 문서에 텍스트 대신 그림, 그래프 혹은 표와 같은 영역이 많은 경우이다. 따라서 레이아웃에 강인한 문서 영상 평활화를 위하여 제안하는 방법은 정렬된 텍스트 라인뿐만 아니라 선분들도 이용한다. 올바르게 평활화 된 선분들은 여전히 일직선의 형태이고, 대부분 가로 혹은 세로 방향으로 정렬되어 있다는 가정 및 관측에 근거하여, 제안하는 방법은 이러한 성질들을 수식화하고 이를 텍스트 라인 기반의 비용 함수와 결합한다. 그리고 비용 함수를 최소화 하는 방법을 통해, 제안하는 방법은 종이의 구부러짐, 카메라 시점, 초점 거리와 같은 평활화 변수들을 추정한다. 또한, 오검출된 텍스트 라인들과 임의의 방향을 가지는 선분들과 같은 이상점(outlier)을 고려하여, 제안하는 방법은 반복적인 단계로 설계된다. 각 단계에서, 정렬 특성을 만족하지 않는 이상점들은 제거되고, 제거되지 않은 텍스트 라인 및 선분들만이 비용함수 최적화에 이용된다. 수행한 실험 결과들은 제안하는 방법이 다양한 레이아웃에 대하여 강인함을 보여준다. 두 번째로는, 본 논문은 장면 텍스트 평활화 방법을 제안한다. 기존 장면 텍스트 평활화 방법들의 경우, 가로/세로 방향의 획, 대칭 형태와 같은 문자가 가지는 고유의 생김새에 관련된 특성을 이용한다. 하지만, 이러한 방법들은 문자들의 정렬 형태는 고려하지 않고, 각각 개별 문자에 대한 특성들만을 이용하기 때문에 여러 문자들로 구성된 텍스트에 대해서 잘 정렬되지 않은 결과를 출력한다. 이러한 문제점을 해결하기 위하여, 제안하는 방법은 문자들의 정렬 정보를 이용한다. 정확하게는, 문자 고유의 모양뿐만 아니라 정렬 특성들도 함께 비용함수로 수식화되고, 비용함수를 최소화하는 방법을 통해서 평활화가 진행된다. 또한, 문자들의 정렬 특성을 수식화하기 위하여, 제안하는 방법은 텍스트를 각각 개별 문자들로 분리하는 문자 분리 또한 수행한다. 그 뒤, 텍스트의 위, 아래 선들을 RANSAC 알고리즘을 이용한 최소 제곱법을 통해 추정한다. 즉, 전체 알고리즘은 문자 분리와 선 추정, 평활화가 반복적으로 수행된다. 제안하는 비용함수는 볼록(convex)형태가 아니고 또한 많은 변수들을 포함하고 있기 때문에, 이를 최적화하기 위하여 Augmented Lagrange Multiplier 방법을 이용한다. 제안하는 방법은 일반 촬영 영상과 합성된 텍스트 영상을 통해 실험이 진행되었고, 실험 결과들은 제안하는 방법이 기존 방법들에 비하여 높은 인식 성능을 보이면서 동시에 시각적으로도 좋은 결과를 보임을 보여준다. 마지막으로, 제안하는 방법은 일반 배경 속의 휘어진 표면 평활화 방법으로도 확장된다. 일반 배경에 대해서, 약병이나 음료수 캔과 같이 원통 형태의 물체는 많이 존재한다. 그들의 표면은 일반 원통 표면(GCS)으로 모델링이 가능하다. 이러한 휘어진 표면들은 많은 문자와 그림들을 포함하고 있지만, 포함된 문자는 문서에 비해서 매우 불규칙적인 구조를 가지고 있다. 따라서 기존의 문서 영상 평활화 방법들로는 일반 배경 속 휘어진 표면 영상을 평활화하기 힘들다. 많은 휘어진 표면은 잘 정렬된 선분들 (테두리 선 혹은 바코드)을 포함하고 있다는 관측에 근거하여, 제안하는 방법은 앞서 제안한 선분들에 대한 함수를 이용하여 휘어진 표면을 평활화한다. 다양한 둥근 물체의 휘어진 표면 영상들에 대한 실험 결과들은 제안하는 방법이 평활화를 정확하게 수행함을 보여준다.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

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

텍스트와 특징점 기반의 목적함수 최적화를 이용한 문서와 텍스트 평활화 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 8. 조남익.There are many techniques and applications that detect and recognize text information in the images, e.g., document retrieval using the camera-captured document image, book reader for visually impaired, and augmented reality based on text recognition. In these applications, the planar surfaces which contain the text are often distorted in the captured image due to the perspective view (e.g., road signs), curvature (e.g., unfolded books), and wrinkles (e.g., old documents). Specifically, recovering the original document texture by removing these distortions from the camera-captured document images is called the document rectification. In this dissertation, new text surface rectification algorithms are proposed, for improving text recognition accuracy and visual quality. The proposed methods are categorized into 3 types depending on the types of the input. The contributions of the proposed methods can be summarized as follows. In the first rectification algorithm, the dense text-lines in the documents are employed to rectify the images. Unlike the conventional approaches, the proposed method does not directly use the text-line. Instead, the proposed method use the discrete representation of text-lines and text-blocks which are the sets of connected components. Also, the geometric distortion caused by page curl and perspective view are modeled as generalized cylindrical surfaces and camera rotation respectively. With these distortion model and discrete representation of the features, a cost function whose minimization yields parameters of the distortion model is developed. In the cost function, the properties of the pages such as text-block alignment, line-spacing, and the straightness of text-lines are encoded. By describing the text features using the sets of discrete points, the cost function can be easily defined and well solved by Levenberg-Marquadt algorithm. Experiments show that the proposed method works well for the various layouts and curved surfaces, and compares favorably with the conventional methods on the standard dataset. The second algorithm is a unified framework to rectify and stitch multiple document images using visual feature points instead of text lines. This is similar to the method employed in general image stitching algorithm. However, the general image stitching algorithm usually assumes fixed center of camera, which is not taken for granted in capturing the document. To deal with the camera motion between images, a new parametric family of motion model is proposed in this dissertation. Besides, to remove the ambiguity in the reference plane, a new cost function is developed to impose the constraints on the reference plane. This enables the estimation of physically correct reference plane without prior knowledge. The estimated reference plane can also be used to rectify the stitching result. Furthermore, the proposed method can be applied to any other planar object such as building facades or mural paintings as well as the camera-captured document image since it employs the general features. The third rectification method is based on scene text detection algorithm, which is independent from the language model. The conventional methods assume that a character consists of a single connected component (CC) like English alphabet. However, this assumption is brittle in the Asian characters such as Korean, Chinese, and Japanese, where a single character consists of several CCs. Therefore, it is difficult to divide CCs into text lines without language model. To alleviate this problem, the proposed method clusters the candidate regions based on the similarity measure considering inter-character relation. The adjacency measure is trained on the data set labeled with the bounding box of text region. Non-text regions that remain after clustering are filtered out in text/non-text classification step. Final text regions are merged or divided into each text line considering the orientation and location. The detected text is rectified using the orientation of text-line and vertical strokes. The proposed method outperforms state-of-the-art algorithms in English as well as Asian characters in the extensive experiments.1 Introduction 1 1.1 Document rectification via text-line based optimization . . . . . . . 2 1.2 A unified approach of rectification and stitching for document images 4 1.3 Rectification via scene text detection . . . . . . . . . . . . . . . . . . 5 1.4 Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Related work 9 2.1 Document rectification . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.1 Document dewarping without text-lines . . . . . . . . . . . . 9 2.1.2 Document dewarping with text-lines . . . . . . . . . . . . . . 10 2.1.3 Text-block identification and text-line extraction . . . . . . . 11 2.2 Document stitching . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Scene text detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3 Document rectification based on text-lines 15 3.1 Proposed approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.1.1 Image acquisition model . . . . . . . . . . . . . . . . . . . . . 16 3.1.2 Proposed approach to document dewarping . . . . . . . . . . 18 3.2 Proposed cost function and its optimization . . . . . . . . . . . . . . 22 3.2.1 Design of Estr(·) . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2.2 Minimization of Estr(·) . . . . . . . . . . . . . . . . . . . . . 23 3.2.3 Alignment type classification . . . . . . . . . . . . . . . . . . 28 3.2.4 Design of Ealign(·) . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2.5 Design of Espacing(·) . . . . . . . . . . . . . . . . . . . . . . . 31 3.3 Extension to unfolded book surfaces . . . . . . . . . . . . . . . . . . 32 3.4 Experimental result . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.4.1 Experiments on synthetic data . . . . . . . . . . . . . . . . . 36 3.4.2 Experiments on real images . . . . . . . . . . . . . . . . . . . 39 3.4.3 Comparison with existing methods . . . . . . . . . . . . . . . 43 3.4.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4 Document rectification based on feature detection 49 4.1 Proposed approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.2 Proposed cost function and its optimization . . . . . . . . . . . . . . 51 4.2.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2.2 Homography between the i-th image and E . . . . . . . . . 52 4.2.3 Proposed cost function . . . . . . . . . . . . . . . . . . . . . . 53 4.2.4 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.2.5 Relation to the model in [17] . . . . . . . . . . . . . . . . . . 55 4.3 Post-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3.1 Classification of two cases . . . . . . . . . . . . . . . . . . . . 56 4.3.2 Skew removal . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.4 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4.1 Quantitative evaluation on metric reconstruction performance 57 4.4.2 Experiments on real images . . . . . . . . . . . . . . . . . . . 58 5 Scene text detection and rectification 67 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 5.1.1 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 5.1.2 Proposed approach . . . . . . . . . . . . . . . . . . . . . . . . 69 5.2 Candidate region detection . . . . . . . . . . . . . . . . . . . . . . . 70 5.2.1 CC extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.2.2 Computation of similarity between CCs . . . . . . . . . . . . 70 5.2.3 CC clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.3 Rectification of candidate region . . . . . . . . . . . . . . . . . . . . 73 5.4 Text/non-text classification . . . . . . . . . . . . . . . . . . . . . . . 76 5.5 Experimental result . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.5.1 Experimental results on ICDAR 2011 dataset . . . . . . . . . 80 5.5.2 Experimental results on the Asian character dataset . . . . . 80 6 Conclusion 83 Bibliography 87 Abstract (Korean) 97Docto