Representation of Web Based Graphics and Equations for the Visually Impaired
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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. -
Reconocimiento De Escritura Lecture 4/5 --- Layout Analysis
Reconocimiento de Escritura Lecture 4/5 | Layout Analysis Daniel Keysers Jan/Feb-2008 Keysers: RES-08 1 Jan/Feb-2008 Outline Detection of Geometric Primitives The Hough-Transform RAST Document Layout Analysis Introduction Algorithms for Layout Analysis A `New' Algorithm: Whitespace Cuts Evaluation of Layout Analyis Statistical Layout Analysis OCR OCR - Introduction OCR fonts Tesseract Sources of OCR Errors Keysers: RES-08 2 Jan/Feb-2008 Outline Detection of Geometric Primitives The Hough-Transform RAST Document Layout Analysis Introduction Algorithms for Layout Analysis A `New' Algorithm: Whitespace Cuts Evaluation of Layout Analyis Statistical Layout Analysis OCR OCR - Introduction OCR fonts Tesseract Sources of OCR Errors Keysers: RES-08 3 Jan/Feb-2008 Detection of Geometric Primitives some geometric entities important for DIA: I text lines I whitespace rectangles (background in documents) Keysers: RES-08 4 Jan/Feb-2008 Outline Detection of Geometric Primitives The Hough-Transform RAST Document Layout Analysis Introduction Algorithms for Layout Analysis A `New' Algorithm: Whitespace Cuts Evaluation of Layout Analyis Statistical Layout Analysis OCR OCR - Introduction OCR fonts Tesseract Sources of OCR Errors Keysers: RES-08 5 Jan/Feb-2008 Hough-Transform for Line Detection Assume we are given a set of points (xn; yn) in the image plane. For all points on a line we must have yn = a0 + a1xn If we want to determine the line, each point implies a constraint yn 1 a1 = − a0 xn xn Keysers: RES-08 6 Jan/Feb-2008 Hough-Transform for Line Detection The space spanned by the model parameters a0 and a1 is called model space, parameter space, or Hough space. -
An Accuracy Examination of OCR Tools
International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8, Issue-9S4, July 2019 An Accuracy Examination of OCR Tools Jayesh Majumdar, Richa Gupta texts, pen computing, developing technologies for assisting Abstract—In this research paper, the authors have aimed to do a the visually impaired, making electronic images searchable comparative study of optical character recognition using of hard copies, defeating or evaluating the robustness of different open source OCR tools. Optical character recognition CAPTCHA. (OCR) method has been used in extracting the text from images. OCR has various applications which include extracting text from any document or image or involves just for reading and processing the text available in digital form. The accuracy of OCR can be dependent on text segmentation and pre-processing algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, a complex background of image etc. From vehicle number plate the authors tried to extract vehicle number by using various OCR tools like Tesseract, GOCR, Ocrad and Tensor flow. The authors in this research paper have tried to diagnose the best possible method for optical character recognition and have provided with a comparative analysis of their accuracy. Keywords— OCR tools; Orcad; GOCR; Tensorflow; Tesseract; I. INTRODUCTION Optical character recognition is a method with which text in images of handwritten documents, scripts, passport documents, invoices, vehicle number plate, bank statements, Fig.1: Functioning of OCR [2] computerized receipts, business cards, mail, printouts of static-data, any appropriate documentation or any II. OCR PROCDURE AND PROCESSING computerized receipts, business cards, mail, printouts of To improve the probability of successful processing of an static-data, any appropriate documentation or any picture image, the input image is often ‘pre-processed’; it may be with text in it gets processed and the text in the picture is de-skewed or despeckled. -
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. -
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 -
Integral Estimation in Quantum Physics
INTEGRAL ESTIMATION IN QUANTUM PHYSICS by Jane Doe A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mathematical Physics Department of Mathematics The University of Utah May 2016 Copyright c Jane Doe 2016 All Rights Reserved The University of Utah Graduate School STATEMENT OF DISSERTATION APPROVAL The dissertation of Jane Doe has been approved by the following supervisory committee members: Cornelius L´anczos , Chair(s) 17 Feb 2016 Date Approved Hans Bethe , Member 17 Feb 2016 Date Approved Niels Bohr , Member 17 Feb 2016 Date Approved Max Born , Member 17 Feb 2016 Date Approved Paul A. M. Dirac , Member 17 Feb 2016 Date Approved by Petrus Marcus Aurelius Featherstone-Hough , Chair/Dean of the Department/College/School of Mathematics and by Alice B. Toklas , Dean of The Graduate School. ABSTRACT Blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah. -
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. -
JETIR Research Journal
© 2019 JETIR June 2019, Volume 6, Issue 6 www.jetir.org (ISSN-2349-5162) IMAGE TEXT CONVERSION FROM REGIONAL LANGUAGE TO SPEECH/TEXT IN LOCAL LANGUAGE 1Sayee Tale,2Manali Umbarkar,3Vishwajit Singh Javriya,4Mamta Wanjre 1Student,2Student,3Student,4Assistant Professor 1Electronics and Telecommunication, 1AISSMS, Institute of Information Technology, Pune, India. Abstract: The drivers who drive in other states are unable to recognize the languages on the sign board. So this project helps them to understand the signs in different language and also they will be able to listen it through speaker. This paper describes the working of two module image processing module and voice processing module. This is done majorly using Raspberry Pi using the technology OCR (optical character recognition) technique. This system is constituted by raspberry Pi, camera, speaker, audio playback module. So the system will help in decreasing the accidents causes due to wrong sign recognition. Keywords: Raspberry pi model B+, Tesseract OCR, camera module, recording and playback module,switch. 1. Introduction: In today’s world life is too important and one cannot loose it simply in accidents. The accident rates in today’s world are increasing day by day. The last data says that 78% accidents were because of driver’s fault. There are many faults of drivers and one of them is that they are unable to read the signs and instructions written on boards when they drove into other states. Though the instruction are for them only but they are not able to make it. So keeping this thing in mind we have proposed a system which will help them to understand the sign boards written in regional language. -
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. -
Manual Archivista 2009/I
Manual Archivista 2009/I c 18th January 2009 by Archivista GmbH, CH-8118 Pfaffhausen Web pages: www.archivista.ch Contents I Introduction 8 4.4 Accessing the manual . 26 4.5 Login WebClient . 26 1 Introduction 9 4.6 Scanning and entering keywords . 26 1.1 Welcome to Archivista . 9 4.7 Rotating pages . 26 1.2 Notes on the manual . 9 4.8 Title search . 27 1.3 Our address . 9 4.9 Full text search . 27 1.4 Previous versions . 9 4.10 Login WebAdmin . 27 1.5 Licensing . 12 4.11 Adding users . 27 4.12 Adding/deleting fields . 27 2 First Steps 18 4.13 Editing the input/search mask . 27 2.1 Introduction . 18 4.14 Activating SSH . 27 2.2 The digital archive . 18 4.15 Activating VNC . 27 2.3 Database, server and client . 18 4.16 Enabling print server (CUPS) . 28 2.4 Tables, records and fields . 19 4.17 Password, Unlock & Restart OCR . 28 2.5 Archivista and working method . 19 4.18 Activating HTTPS . 28 2.6 Tips for archiving . 20 2.7 Archive, pages and documents . 20 5 Tutorial RichClient 29 2.8 The Archivista document . 20 5.1 Archivista in 90 Seconds . 29 5.2 Adding Documents . 29 3 Installation 22 5.3 Search . 29 3.1 ArchivistaBox . 22 5.4 Extended Functions . 30 3.2 Virtual (Box) . 22 5.5 Users & Fields . 31 3.3 OpenSource (Box) . 22 5.6 Databases, fields and barcodes . 31 3.4 OpenSource (Windows) . 22 III ArchivistaBox 33 II Tutorials 25 6 Introduction 34 4 Introduction 26 6.1 Advantages . -
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 ..................................................................................................