Robyn Michelle Hoggan v. Ranee Boley Fleming, Karen B. Gamonal, Joyce H. Crockett, John D Hoggan, Leo V. Jolley, Rosalee J Keele, The Estate of Kriste H. Street, The Estate of Elizabeth D. Jolley Gardner, and John Does : Brief of Appellant

APPEAL FROM FINAL ORDER GRANTING DEFENDANTS/APPELLEES\u27 MOTION TO DISMISS, OF THE UTAH THIRD JUDICIAL DISTRICT COURT, IN AND FOR SALT LAKE COUNTY, THE HONORABLE DENISE P. LINDBERG PRESIDIN

Lawrence M. Russell, Russell/Packard Development, Inc., Saratoga Springs Development, L.C., Merlin Smith, and Margie Smith v. John J. Thomas and PRP Development, L.C.: Brief of Appellant

Appeal from the Fourth District Court of the State of Uta

Commonwealth Times 2002-09-19

https://scholarscompass.vcu.edu/com/2277/thumbnail.jp

State of Utah v. Michael J. Birkeland : Brief of Appellant

BRIEF OF APPELLANT APPEAL FROM THE FOURTH DISTRICT COURT, UTAH COUNTY, STATE OF UTAH, FROM THE RESTITUTION ORDER FOLLOWING A NO CONTEST PLEA TO THEFT, A CLASS A MISDEMEANOR, BEFORE THE HONORABLE CLAUDIA LAYCOC

U.P.C., Inc. v. R.O.A. General, Inc : Brief of Appellant

APPEAL FROM THE THIRD DISTRICT COURT, SALT LAKE COUNTY, JUDGE WILLIAM\u27 B. BOHLIN

Standard Optical Company v. Salt Lake Corporation : Unknown

Appeal from the Judgment of the Third Judicial District Court for Salt Lake County, Utah, The Honorable Bryant H. Croft, Judg

Stephen Whitehead, Deborah Whitehead v. American Motors Sales Corporation, Jeep Corporation, Larry Anderson, Variable Annuity Life Insurance Company

Petitioner\u27s Brief on Rehearin

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information

ONG International Inc., D&D Management, and David L. Alldredge v. 11th Avenue Corporation, f/k/a Salt Lake Memorial Mausoleum; and Keith E. Garner : Brief of Appellant

APPEAL FROM JUDGMENTS OF THE THIRD JUDICIAL DISTRICT COURT SALT LAKE COUNTY, STATE OF UTAH, JUDGE J. DENNIS FREDERIC

Application of vector-valued rational approximations to the matrix eigenvalue problem and connections with Krylov subspace methods

Let F(z) be a vectored-valued function F: C approaches C sup N, which is analytic at z=0 and meromorphic in a neighborhood of z=0, and let its Maclaurin series be given. We use vector-valued rational approximation procedures for F(z) that are based on its Maclaurin series in conjunction with power iterations to develop bona fide generalizations of the power method for an arbitrary N X N matrix that may be diagonalizable or not. These generalizations can be used to obtain simultaneously several of the largest distinct eigenvalues and the corresponding invariant subspaces, and present a detailed convergence theory for them. In addition, it is shown that the generalized power methods of this work are equivalent to some Krylov subspace methods, among them the methods of Arnoldi and Lanczos. Thus, the theory provides a set of completely new results and constructions for these Krylov subspace methods. This theory suggests at the same time a new mode of usage for these Krylov subspace methods that were observed to possess computational advantages over their common mode of usage