Document Image Analysis Techniques for Handwritten Text Segmentation, Document Image Rectification and Digital Collation

Document image analysis comprises all the algorithms and techniques that are utilized to convert an image of a document to a computer readable description. In this work we focus on three such techniques, namely (1) Handwritten text segmentation (2) Document image rectification and (3) Digital Collation. Offline handwritten text recognition is a very challenging problem. Aside from the large variation of different handwriting styles, neighboring characters within a word are usually connected, and we may need to segment a word into individual characters for accurate character recognition. Many existing methods achieve text segmentation by evaluating the local stroke geometry and imposing constraints on the size of each resulting character, such as the character width, height and aspect ratio. These constraints are well suited for printed texts, but may not hold for handwritten texts. Other methods apply holistic approach by using a set of lexicons to guide and correct the segmentation and recognition. This approach may fail when the domain lexicon is insufficient. In the first part of this work, we present a new global non-holistic method for handwritten text segmentation, which does not make any limiting assumptions on the character size and the number of characters in a word. We conduct experiments on real images of handwritten texts taken from the IAM handwriting database and compare the performance of the presented method against an existing text segmentation algorithm that uses dynamic programming and achieve significant performance improvement. Digitization of document images using OCR based systems is adversely affected if the image of the document contains distortion (warping). Often, costly and precisely calibrated special hardware such as stereo cameras, laser scanners, etc. are used to infer the 3D model of the distorted image which is used to remove the distortion. Recent methods focus on creating a 3D shape model based on 2D distortion informa- tion obtained from the document image. The performance of these methods is highly dependent on estimating an accurate 2D distortion grid. These methods often affix the 2D distortion grid lines to the text line, and as such, may suffer in the presence of unreliable textual cues due to preprocessing steps such as binarization. In the domain of printed document images, the white space between the text lines carries as much information about the 2D distortion as the text lines themselves. Based on this intuitive idea, in the second part of our work we build a 2D distortion grid from white space lines, which can be used to rectify a printed document image by a dewarping algorithm. We compare our presented method against a state-of-the-art 2D distortion grid construction method and obtain better results. We also present qualitative and quantitative evaluations for the presented method. Collation of texts and images is an indispensable but labor-intensive step in the study of print materials. It is an often used methodology by textual scholars when the manuscript of the text does not exist. Although various methods and machines have been designed to assist in this labor, it still remains an expensive and time- consuming process, often requiring travel to distant repositories for the painstaking visual examination of multiple original copies. Efforts to digitize collation have so far depended on first transcribing the texts to be compared, thus introducing into the process more labor and expense, and also more potential error. Digital collation will instead automate the first stages of collation directly from the document images of the original texts, thereby speeding the process of comparison. We describe such a novel framework for digital collation in the third part of this work and provide qualitative results

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

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

Geometric correction of historical Arabic documents

Geometric deformations in historical documents significantly influence the success of both Optical Character Recognition (OCR) techniques and human readability. They may have been introduced at any time during the life cycle of a document, from when it was first printed to the time it was digitised by an imaging device. This Thesis focuses on the challenging domain of geometric correction of Arabic historical documents, where background research has highlighted that existing approaches for geometric correction of Latin-script historical documents are not sensitive to the characteristics of text in Arabic documents and therefore cannot be applied successfully. Text line segmentation and baseline detection algorithms have been investigated to propose a new more suitable one for warped Arabic historical document images. Advanced ideas for performing dewarping and geometric restoration on historical Arabic documents, as dictated by the specific characteristics of the problem have been implemented.In addition to developing an algorithm to detect accurate baselines of historical printed Arabic documents the research also contributes a new dataset consisting of historical Arabic documents with different degrees of warping severity.Overall, a new dewarping system, the first for Historical Arabic documents, has been developed taking into account both global and local features of the text image and the patterns of the smooth distortion between text lines. By using the results of the proposed line segmentation and baseline detection methods, it can cope with a variety of distortions, such as page curl, arbitrary warping and fold

Captured open book image de-warping and shading correction using 3D depth information

Various three dimensional (3D) measuring or capturing devices are introduced to the society recently, and there are abundant possibilities that we can take advantage of this new technology. In this research, we worked on one useful application: to correct the distortion due to the curved shape of the pages of an open book in captured images using of depth information. This work is relevant to camera-based capture devices that can use a projector to cast structured light patterns to provide depth information. In order to improve the visual quality of captured documents, we established our algorithm from two perspectives. First, we deal with the shading situation in the captured image as a result of the non-uniform lighting condition. The shading correction is based on the shading information of the margin of the document, or based on the estimated relative position of each piece of the scanned open book to the active illumination. The open book will look like it is captured under a uniform lighting source in the corrected images. Next, we handle the geometric distortion. The 3D shape reconstruction methods and geometric rectification are used to flatten the curvature of an open book. The models we used exploit specific prior assumptions about the nature of the printed material that is captured. The warped text line can be straightened after this rectification. The overall readability improvement in captured open book images obtained by using our method can be observed in the experimental results

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

Processing Camera-captured Document Images: Geometric Rectification, Mosaicing, and Layout Structure Recognition

This dissertation explores three topics: 1) geometric rectification of cameracaptured document images, 2) camera-captured document mosaicing, and 3) layout structure recognition. The first two topics pertain to camera-based document image analysis, a new trend within the OCR community. Compared to typical scanners,cameras offer convenient, flexible, portable, and non-contact image capture, which enables many new applications and breathes new life into existing ones. The third topic is related to the need for efficient metadata extraction methods, critical for managing digitized documents. The kernel of our geometric rectification framework is a novel method for estimating document shape from a single camera-captured image. Our method uses texture flows detected in printed text areas and is insensitive to occlusion. Classification of planar versus curved documents is done automatically. For planar pages, we obtain full metric rectification. For curved pages, we estimate a planar-strip approximation based on properties of developable surfaces. Our method can process any planar or smoothly curved document captured from an arbitrary position without requiring 3D data, metric data, or camera calibration. For the second topic, we design a novel registration method for document images, which produces good results in difficult situations including large displacements, severe projective distortion, small overlapping areas, and lack of distinguishable feature points. We implement a selective image composition method that outperforms conventional image blending methods in overlapping areas. It eliminates double images caused by mis-registration and preserves the sharpness in overlapping areas. We solve the third topic with a graph-based model matching framework. Layout structures are modeled by graphs, which integrate local and global features and are extensible to new features in the future. Our model can handle large variation within a class and subtle differences between classes. Through graph matching, the layout structure of a document is discovered. Our layout structure recognition technique accomplishes document classification and logical component labeling at the same time. Our model learning method enables a model to adapt to changes in classes over time