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OCR Pwds and Assistive Qatari Using OCR Issue No
Arabic Optical State of the Smart Character Art in Arabic Apps for Recognition OCR PWDs and Assistive Qatari using OCR Issue no. 15 Technology Research Nafath Efforts Page 04 Page 07 Page 27 Machine Learning, Deep Learning and OCR Revitalizing Technology Arabic Optical Character Recognition (OCR) Technology at Qatar National Library Overview of Arabic OCR and Related Applications www.mada.org.qa Nafath About AboutIssue 15 Content Mada Nafath3 Page Nafath aims to be a key information 04 Arabic Optical Character resource for disseminating the facts about Recognition and Assistive Mada Center is a private institution for public benefit, which latest trends and innovation in the field of Technology was founded in 2010 as an initiative that aims at promoting ICT Accessibility. It is published in English digital inclusion and building a technology-based community and Arabic languages on a quarterly basis 07 State of the Art in Arabic OCR that meets the needs of persons with functional limitations and intends to be a window of information Qatari Research Efforts (PFLs) – persons with disabilities (PWDs) and the elderly in to the world, highlighting the pioneering Qatar. Mada today is the world’s Center of Excellence in digital work done in our field to meet the growing access in Arabic. Overview of Arabic demands of ICT Accessibility and Assistive 11 OCR and Related Through strategic partnerships, the center works to Technology products and services in Qatar Applications enable the education, culture and community sectors and the Arab region. through ICT to achieve an inclusive community and educational system. The Center achieves its goals 14 Examples of Optical by building partners’ capabilities and supporting the Character Recognition Tools development and accreditation of digital platforms in accordance with international standards of digital access. -
Die Bemiddeling Van 21Ste- Eeuse Navorsing in Die Afrikaanse Letterkunde
LitNet Akademies Jaargang 8(2) – Augustus 2011 Die digitalisering van NALN se knipselversameling: Die bemiddeling van 21ste- eeuse navorsing in die Afrikaanse letterkunde Burgert A. Senekal Departement Afrikaans en Nederlands, Duits en Frans Universiteit van die Vrystaat Summary The digitisation of NALN’s collection of newspaper clippings: Enabling 21st century research in Afrikaans literature The contemporary world is an extremely complex environment due to globalisation and the internet. Within this globalised interdependent framework, researchers, both in an academic context and in nonacademic settings such as business, cannot expect to succeed without incorporating resources that extend the reach of the individual's environment and expedite the processing of information. Not only has the amount of information circling the globe increased rapidly over the past two decades, but museums and libraries have had to downsize their staff, which effectively means that fewer resources are available to handle more information and in addition serve a larger population. If the humanities’ basic tasks are “preserving, reconstructing, transmitting, and interpreting the human record” (Frischer 2009:15), technology is the key, enabling research across international borders, distributing data, findings and insights globally, and managing the deluge of information that is characteristic of the 21stcentury world. Within this context, digitisation – the process of converting analogue documents to a digital format – occupies a privileged position, enabling the distribution of information globally (and thereby contributing to information overload), as well as safeguarding the preservation of important historical material. Digitisation has become a global trend, and although many South African heritage institutions have been slow to make the transition from analogue to digital – mainly because of budget constraints – museums and archives in South Africa have realised the potential that digitisation holds, and are now digitising their collections. -
Extracción De Eventos En Prensa Escrita Uruguaya Del Siglo XIX Por Pablo Anzorena Manuel Laguarda Bruno Olivera
UNIVERSIDAD DE LA REPÚBLICA Extracción de eventos en prensa escrita Uruguaya del siglo XIX por Pablo Anzorena Manuel Laguarda Bruno Olivera Tutora: Regina Motz Informe de Proyecto de Grado presentado al Tribunal Evaluador como requisito de graduación de la carrera Ingeniería en Computación en la Facultad de Ingeniería 1 1. Resumen En este proyecto, se plantea el diseño y la implementación de un sistema de extracción de eventos en prensa uruguaya del siglo XIX digitalizados en formato de imagen, generando clusters de eventos agrupados según su similitud semántica. La solución propuesta se divide en 4 módulos: módulo de preprocesamiento compuesto por el OCR y un corrector de texto, módulo de extracción de eventos implementado en Python y utilizando Freeling1, módulo de clustering de eventos implementado en Python utilizando Word Embeddings y por último el módulo de etiquetado de los clusters también utilizando Python. Debido a la cantidad de ruido en los datos que hay en los diarios antiguos, la evaluación de la solución se hizo sobre datos de prensa digital de la actualidad. Se evaluaron diferentes medidas a lo largo del proceso. Para la extracción de eventos se logró conseguir una Precisión y Recall de un 56% y 70% respectivamente. En el caso del módulo de clustering se evaluaron las medidas de Silhouette Coefficient, la Pureza y la Entropía, dando 0.01, 0.57 y 1.41 respectivamente. Finalmente se etiquetaron los clusters utilizando como etiqueta las secciones de los diarios de la actualidad, realizándose una evaluación del etiquetado. 1 http://nlp.lsi.upc.edu/freeling/demo/demo.php 2 Índice general 1. -
Opentext™ Teleform Release Notes Rev.: June 2020 This Documentation Has Been Created for Software Version 20.3
OpenText™ TeleForm™ Release Notes OpenText TeleForm Documentation Release 20.3 Release Notes OpenText™ TeleForm Release Notes Rev.: June 2020 This documentation has been created for software version 20.3. It is also valid for subsequent software versions as long as no new document version is shipped with the product or is published at https://knowledge.opentext.com. Open Text Corporation 275 Frank Tompa Drive, Waterloo, Ontario, Canada, N2L 0A1 Tel: +1-519-888-7111 Toll Free Canada/USA: 1-800-499-6544 International: +800-4996-5440 Fax: +1-519-888-0677 Support: https://support.opentext.com For more information, visit https://www.opentext.com Copyright © 2020 Open Text. All rights reserved. Trademarks owned by Open Text. One or more patents may cover this product. For more information, please visit, https://www.opentext.com/patents Disclaimer No Warranties and Limitation of Liability Every effort has been made to ensure the accuracy of the features and techniques presented in this publication. However, Open Text Corporation and its affiliates accept no responsibility and offer no warranty whether expressed or implied, for the accuracy of this publication. Page 2 of 3 OpenText TeleForm (20.3) Release Notes Contents 1 Introduction 1 1.1 Release notes revision history 1 2 About TeleForm 2 2.1 New features 2 2.1.1 Recognition enhancements 2 2.1.2 Updated platform support 3 2.1.3 Documentation updates 3 2.2 Discontinued and deprecated features 4 2.2.1 Discontinued platform support 4 2.2.2 Deprecated features 4 3 Packaging and documentation 6 -
Tesseract Als Komponente Im OCR-D-Workflow
- Projekt Optimierter Einsatz von OCR-Verfahren – Tesseract als Komponente im OCR-D-Workflow OCR Noah Metzger, Stefan Weil Universitätsbibliothek Mannheim 30.07.2019 Prozesskette Forschungdaten aus Digitalisaten Digitalisierung/ Text- Struktur- Vorverarbeitung erkennung parsing (OCR) Strukturierung Bücher Generierung Generierung der digitalen Inhalte digitaler Ausgangsformate der digitalen Inhalte (Datenextraktion) 28.03.2019 2 OCR Software (Übersicht) kommerzielle fett = eingesetzt in Bibliotheken Software ABBYY Finereader Tesseract freie Software BIT-Alpha OCRopus / Kraken / Readiris Calamari OmniPage CuneiForm … … Adobe Acrobat CorelDraw ABBYY Cloud OCR Microsoft OneNote Google Cloud Vision … Microsoft Azure Computer Vision OCR.space Online OCR … Cloud OCR 28.03.2019 3 Tesseract OCR • Open Source • Komplettlösung „All-in-1“ • Mehr als 100 Sprachen / mehr als 30 Schriften • Liest Bilder in allen gängigen Formaten (nicht PDF!) • Erzeugt Text, PDF, hOCR, ALTO, TSV • Große, weltweite Anwender-Community • Technologisch aktuell (Texterkennung mit neuronalem Netz) • Aktive Weiterentwicklung u. a. im DFG-Projekt OCR-D 28.03.2019 4 Tesseract an der UB Mannheim • Verwendung im DFG-Projekt „Aktienführer“ https://digi.bib.uni-mannheim.de/aktienfuehrer/ • Volltexte für Deutscher Reichsanzeiger und Vorgänger https://digi.bib.uni-mannheim.de/periodika/reichsanzeiger • DFG-Projekt „OCR-D“ http://www.ocr-d.de/, Modulprojekt „Optimierter Einsatz von OCR-Verfahren – Tesseract als Komponente im OCR-D-Workflow“: Schnittstellen, Stabilität, Performance -
Paperport 14 Getting Started Guide Version: 14.7
PaperPort 14 Getting Started Guide Version: 14.7 Date: 2019-09-01 Table of Contents Legal notices................................................................................................................................................4 Welcome to PaperPort................................................................................................................................5 Accompanying programs....................................................................................................................5 Install PaperPort................................................................................................................................. 5 Activate PaperPort...................................................................................................................6 Registration.............................................................................................................................. 6 Learning PaperPort.............................................................................................................................6 Minimum system requirements.......................................................................................................... 7 Key features........................................................................................................................................8 About PaperPort.......................................................................................................................................... 9 The PaperPort desktop..................................................................................................................... -
Introduction to Digital Humanities Course 3 Anca Dinu
Introduction to Digital Humanities course 3 Anca Dinu DH master program, University of Bucharest, 2019 Primary source for the slides: THE DIGITAL HUMANITIES A Primer for Students and Scholars by Eileen Gardiner and Ronald G. Musto, Cambridge University Press, 2015 DH Tools • Tools classification by: • the object they process (text, image, sound, etc.) • the task they are supposed to perform, output or result. • Projects usually require multiple tools either in parallel or in succession. • To learn how to use them - free tutorials on product websites, on YouTube and other. DH Tools Tools classification by the object they process • Text-based tools: • Text Analysis: • The simplest and most familiar example of text analysis is the document comparison feature in Microsoft Word (taking two different versions of the same document and highlight the differences); • Dedicated text analysis tools create concordances, keyword density/prominence, visualizing patterns, etc. (for instance AntConc) DH Tools • Text Annotation: • On a basic level, digital text annotation is simply adding notes or glosses to a document, for instance, putting comments on a PDF file for personal use. • For complex projects, there are interfaces specifically designed for annotations. • Example: FoLiA-Linguistic-Annotation-Tool https://pypi.org/project/FoLiA-Linguistic-Annotation-Tool/ DH Tools • Text Conversion and Encoding tools: • Every text in digital format is encoded with tags, whether this is apparent to the user or not. • Everything from font size, bold, italics and underline, line and paragraph spacing, justification and superscripts, to meta-data as title and author are the result of such coding tags. • Common encoding standards: XML, HTML, TEI, RTF, etc. -
Kofax Omnipage Ultimate
PRODUCT SUMMARY Kofax OmniPage Ultimate Don't just convert documents —transform them The Speed, Quality and Features to Save Valuable Time, Reduce Costs, Increase Productivity, and Get the Most out of Your Scanning Devices The continued use of paper slows organizations down and frequently leads to “knowledge re-creation.” Not only does this waste time—as you are forced to re-create information from existing paper or search for misplaced documents—but it is also a drain on overall productivity and profitability. Additionally, small businesses and individuals underutilize millions of scanning devices that could save valuable time and effort. Finally, PDF documents—the digital version of paper—are prolific and can be difficult to edit or repurpose. The question remains: “How can you get the document into the format you want without having to do extensive re-typing or re-editing?” The answer is Kofax OmniPage Ultimate. Convert paper, PDF files and forms into documents you can automatically send to others, edit on your PC or archive in a document repository. Amazing accuracy, support for virtually any scanner, the best tools to customize your process, and automatic document routing make it the perfect choice to maximize productivity. Omnipage Ultimate Advantages Maximize PDF search The eDiscovery Assistant for searchable PDF is a revolution in safely converting a single PDF or batches of PDFs of all types into completely searchable documents. Now you don’t have to open PDF files individually, or use an OCR process that may unintentionally wipe out valuable information. Improve accuracy OmniPage Ultimate now delivers an average increase of 25% in OCR accuracy of digital camera images. -
Representation of Web Based Graphics and Equations for the Visually Impaired
TH NATIONAL ENGINEERING CONFERENCE 2012, 18 ERU SYMPOSIUM, FACULTY OF ENGINEERING, UNIVERSITY OF MORATUWA, SRI LANKA Representation of Web based Graphics and Equations for the Visually Impaired C.L.R. Gunawardhana, H.M.M. Hasanthika, T.D.G.Piyasena,S.P.D.P.Pathirana, S. Fernando, A.S. Perera, U. Kohomban Abstract With the extensive growth of technology, it is becoming prominent in making learning more interactive and effective. Due to the use of Internet based resources in the learning process, the visually impaired community faces difficulties. In this research we are focusing on developing an e-Learning solution that can be accessible by both normal and visually impaired users. Accessibility to tactile graphics is an important requirement for visually impaired people. Recurrent expenditure of the printers which support graphic printing such as thermal embossers is beyond the budget for most developing countries which cannot afford such a cost for printing images. Currently most of the books printed using normal text Braille printers ignore images in documents and convert only the textual part. Printing images and equations using normal text Braille printers is a main research area in the project. Mathematical content in a forum and simple images such as maps in a course page need to be made affordable using the normal text Braille printer, as these functionalities are not available in current Braille converters. The authors came up with an effective solution for the above problems and the solution is presented in this paper. 1 1. Introduction In order to images accessible to visually impaired people the images should be converted into a tactile In this research our main focus is to make e-Learning format. -
Choosing Character Recognition Software To
CHOOSING CHARACTER RECOGNITION SOFTWARE TO SPEED UP INPUT OF PERSONIFIED DATA ON CONTRIBUTIONS TO THE PENSION FUND OF UKRAINE Prepared by USAID/PADCO under Social Sector Restructuring Project Kyiv 1999 <CHARACTERRECOGNITION_E_ZH.DOC> printed on June 25, 2002 2 CONTENTS LIST OF ACRONYMS.......................................................................................................................................................................... 3 INTRODUCTION................................................................................................................................................................................ 4 1. TYPES OF INFORMATION SYSTEMS....................................................................................................................................... 4 2. ANALYSIS OF EXISTING SYSTEMS FOR AUTOMATED TEXT RECOGNITION................................................................... 5 2.1. Classification of automated text recognition systems .............................................................................................. 5 3. ATRS BASIC CHARACTERISTICS............................................................................................................................................ 6 3.1. CuneiForm....................................................................................................................................................................... 6 3.1.1. Some information on Cognitive Technologies .................................................................................................. -
Downloadable
UC Berkeley Research and Occasional Papers Series Title THE UC CLIOMETRIC HISTORY PROJECT AND FORMATTED OPTICAL CHARACTER RECOGNITION Permalink https://escholarship.org/uc/item/9xz1748q Author Bleemer, Zachary Publication Date 2018-02-10 eScholarship.org Powered by the California Digital Library University of California Research & Occasional Paper Series: CSHE.3.18 UNIVERSITY OF CALIFORNIA, BERKELEY http://cshe.berkeley.edu/ The University of California@150* THE UC CLIOMETRIC HISTORY PROJECT AND FORMATTED OPTICAL CHARACTER RECOGNITION February 2018 Zachary Bleemer** UC Berkeley Copyright 2018 Zachary Bleemer, all rights reserved. ABSTRACT In what ways—and to what degree—have universities contributed to the long-run growth, health, economic mobility, and gender/ethnic equity of their students’ communities and home states? The University of California ClioMetric History Project (UC- CHP), based at the Center for Studies in Higher Education, extends prior research on this question in two ways. First, we have developed a novel digitization protocol—formatted optical character recognition (fOCR)—which transforms scanned structured and semi-structured texts like university directories and catalogs into high-quality computer-readable databases. We use fOCR to produce annual databases of students (1890s to 1940s), faculty (1900 to present), course descriptions (1900 to present), and detailed budgets (1911-2012) for many California universities. Digitized student records, for example, illuminate the high proportion of 1900s university students who were female and from rural areas, as well as large family income differences between male and female students and between students at public and private universities. Second, UC- CHP is working to photograph, process with fOCR, and analyze restricted student administrative records to construct a comprehensive database of California university students and their enrollment behavior. -
Analiza I Optičko Prepoznavanje Rukopisa S Herbarijskih Etiketa U Zbirci Herbarium Croaticum
SVEUČILIŠTE U ZAGREBU FILOZOFSKI FAKULTET ODSJEK ZA INFORMACIJSKE I KOMUNIKACIJSKE ZNANOSTI KATEDRA ZA ARHIVISTIKU I DOKUMENTALISTIKU Marta Dević Hameršmit Analiza i optičko prepoznavanje rukopisa s herbarijskih etiketa u zbirci Herbarium Croaticum Diplomski rad Mentor: dr. sc. Hrvoje Stančić, red. prof. Neposredni voditelj: Vedran Šegota, dipl. ing. bio. (Botanički zavod, Biološki odsjek, PMF) Zagreb, srpanj 2018. Sadržaj 1. Uvod ....................................................................................................................................... 1 1.1. Optičko prepoznavanje .................................................................................................... 2 1.1.1. Optičko prepoznavanje znakova .............................................................................. 2 1.1.2. Inteligentno prepoznavanje znakova ........................................................................ 3 1.1.3. Prepoznavanje rukopisa ............................................................................................ 4 1.2. Herbarij ............................................................................................................................ 5 1.2.1. Herbarijske zbirke .................................................................................................... 6 1.2.2. Herbarijske etikete .................................................................................................... 6 2. Ciljevi ....................................................................................................................................