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a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity-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

Automatic Multiple Choice Examination Questions Marking and Grade Generator Software

This paper discusses a feasible software solution that enables automatic marking andgrading of scripts. Technology keeps expanding, and more advanced innovations arebeing implemented with time. The marking and allocation of grades for examina-tion scripts through human efforts are gradually becoming a thing of the past. Hence,machines and software applications are introduced to make the entire marking andgrading of examination scripts more efficient, fast, and less tedious. Computer visionis an artificial intelligence (AI) knowledge domain that ensures devices obtain usefulinformation from digital images, videos, and other visual inputs. Image processingand recognition, a unique part of computer vision alongside the python program-ming language and the OpenCV library was employed for this project. These are themost used in developing most recent applications that utilize, to some extent, arti-ficial intelligence to attain specific desired results. The result of the project seeksto develop a maintainable android software application that uses image processingtechnology to scan patterns or images and grades results of multiple-choice questionscripts based on a set marking scheme. This ensures that desired results are obtainedwhile increasing efficiency and productivity

Real-time target and pose recognition for 3-D graphical overlay

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 47-48).by Jeffrey M. Levine.M.Eng

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

INFORMATION TECHNOLOGY FOR NEXT-GENERATION OF SURGICAL ENVIRONMENTS

Minimally invasive surgeries (MIS) are fundamentally constrained by image quality,access to the operative field, and the visualization environment on which thesurgeon relies for real-time information. Although invasive access benefits the patient,it also leads to more challenging procedures, which require better skills andtraining. Endoscopic surgeries rely heavily on 2D interfaces, introducing additionalchallenges due to the loss of depth perception, the lack of 3-Dimensional imaging,and the reduction of degrees of freedom.By using state-of-the-art technology within a distributed computational architecture,it is possible to incorporate multiple sensors, hybrid display devices, and3D visualization algorithms within a exible surgical environment. Such environmentscan assist the surgeon with valuable information that goes far beyond what iscurrently available. In this thesis, we will discuss how 3D visualization and reconstruction,stereo displays, high-resolution display devices, and tracking techniques arekey elements in the next-generation of surgical environments

Seamless Positioning and Navigation in Urban Environment

L'abstract è presente nell'allegato / the abstract is in the attachmen

Registration and categorization of camera captured documents

Camera captured document image analysis concerns with processing of documents captured with hand-held sensors, smart phones, or other capturing devices using advanced image processing, computer vision, pattern recognition, and machine learning techniques. As there is no constrained capturing in the real world, the captured documents suffer from illumination variation, viewpoint variation, highly variable scale/resolution, background clutter, occlusion, and non-rigid deformations e.g., folds and crumples. Document registration is a problem where the image of a template document whose layout is known is registered with a test document image. Literature in camera captured document mosaicing addressed the registration of captured documents with the assumption of considerable amount of single chunk overlapping content. These methods cannot be directly applied to registration of forms, bills, and other commercial documents where the fixed content is distributed into tiny portions across the document. On the other hand, most of the existing document image registration methods work with scanned documents under affine transformation. Literature in document image retrieval addressed categorization of documents based on text, figures, etc. However, the scalability of existing document categorization methodologies based on logo identification is very limited. This dissertation focuses on two problems (i) registration of captured documents where the overlapping content is distributed into tiny portions across the documents and (ii) categorization of captured documents into predefined logo classes that scale to large datasets using local invariant features. A novel methodology is proposed for the registration of user defined Regions Of Interest (ROI) using corresponding local features from their neighborhood. The methodology enhances prior approaches in point pattern based registration, like RANdom SAmple Consensus (RANSAC) and Thin Plate Spline-Robust Point Matching (TPS-RPM), to enable registration of cell phone and camera captured documents under non-rigid transformations. Three novel aspects are embedded into the methodology: (i) histogram based uniformly transformed correspondence estimation, (ii) clustering of points located near the ROI to select only close by regions for matching, and (iii) validation of the registration in RANSAC and TPS-RPM algorithms. Experimental results on a dataset of 480 images captured using iPhone 3GS and Logitech webcam Pro 9000 have shown an average registration accuracy of 92.75% using Scale Invariant Feature Transform (SIFT). Robust local features for logo identification are determined empirically by comparisons among SIFT, Speeded-Up Robust Features (SURF), Hessian-Affine, Harris-Affine, and Maximally Stable Extremal Regions (MSER). Two different matching methods are presented for categorization: matching all features extracted from the query document as a single set and a segment-wise matching of query document features using segmentation achieved by grouping area under intersecting dense local affine covariant regions. The later approach not only gives an approximate location of predicted logo classes in the query document but also helps to increase the prediction accuracies. In order to facilitate scalability to large data sets, inverted indexing of logo class features has been incorporated in both approaches. Experimental results on a dataset of real camera captured documents have shown a peak 13.25% increase in the F–measure accuracy using the later approach as compared to the former

Reconstructing the Past in 3D Using Historical Aerial Imgery

Historical aerial film images are a valuable record of the past, and are useful as a baseline for change detection and landcover analysis. To be useful in GIS analysis the images must be oriented to a spatial reference system. This is challenging as historical imagery is often missing flight and camera information. Traditional photogrammetric processing techniques exist to overcome these challenges, but they require specialized knowledge, time and expense to complete. Because of this, many collections of historical images are left unprocessed. This project produced a method to quickly standardize the photos, spatially orient them, correct them for distortion effects, and extract a digital surface model from the overlapping image series using Pix4D Professional. The horizontal accuracy met National Map Accuracy Standards when the Pix 4D process was combined with traditional georeferencing. The workflow was faster than traditional methods due to economies of scale in the new process