An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow For
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Advanced OCR with Omnipage and Finereader
AAddvvHighaa Technn Centerccee Trainingdd UnitOO CCRR 21050 McClellan Rd. Cupertino, CA 95014 www.htctu.net Foothill – De Anza Community College District California Community Colleges Advanced OCR with OmniPage and FineReader 10:00 A.M. Introductions and Expectations FineReader in Kurzweil Basic differences: cost Abbyy $300, OmniPage Pro $150/Pro Office $600; automating; crashing; graphic vs. text 10:30 A.M. OCR program: Abbyy FineReader www.abbyy.com Looking at options Working with TIFF files Opening the file Zoom window Running OCR layout preview modifying spell check looks for barcodes Blocks Block types Adding to blocks Subtracting from blocks Reordering blocks Customize toolbars Adding reordering shortcut to the tool bar Save and load blocks Eraser Saving Types of documents Save to file Formats settings Optional hyphen in Word remove optional hyphen (Tools > Format Settings) Tables manipulating Languages Training 11:45 A.M. Lunch 1:00 P.M. OCR program: ScanSoft OmniPage www.scansoft.com Looking at options Languages Working with TIFF files SET Tools (see handout) www.htctu.net rev. 9/27/2011 Opening the file View toolbar with shortcut keys (View > Toolbar) Running OCR On-the-fly zoning modifying spell check Zone type Resizing zones Reordering zones Enlargement tool Ungroup Templates Saving Save individual pages Save all files in one document One image, one document Training Format types Use true page for PDF, not Word Use flowing page or retain fronts and paragraphs for Word Optional hyphen in Word Tables manipulating Scheduler/Batch manager: Workflow Speech Saving speech files (WAV) Creating a Workflow 2:30 P.M. Break 2:45 P.M. -
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. -
ABBYY Finereader Engine OCR
ABBYY FineReader Engine Performance Guide Integrating optical character recognition (OCR) technology will effectively extend the functionality of your application. Excellent performance of the OCR component is one of the key factors for high customer satisfaction. This document provides information on general OCR performance factors and the possibilities to optimize them in the Software Development Kit ABBYY FineReader Engine. By utilizing its advanced capabilities and options, the high OCR performance can be improved even further for optimal customer experience. When measuring OCR performance, there are two major parameters to consider: RECOGNITION ACCURACY PROCESSING SPEED Which Factors Influence the OCR Accuracy and Processing Speed? Image type and Image image source quality OCR accuracy and Processing System settings processing resources speed Document Application languages architecture Recognition speed and recognition accuracy can be significantly improved by using the right parameters in ABBYY FineReader Engine. Image Type and Image Quality Images can come from different sources. Digitally created PDFs, screenshots of computer and tablet devices, image Key factor files created by scanners, fax servers, digital cameras Image for OCR or smartphones – various image sources will lead to quality = different image types with different level of image quality. performance For example, using the wrong scanner settings can cause “noise” on the image, like random black dots or speckles, blurred and uneven letters, or skewed lines and shifted On the other hand, processing ‘high-quality images’ with- table borders. In terms of OCR, this is a ‘low-quality out distortions reduces the processing time. Additionally, image’. reading high-quality images leads to higher accuracy results. Processing low-quality images requires high computing power, increases the overall processing time and deterio- Therefore, it is recommended to use high-quality images rates the recognition results. -
Ocr: a Statistical Model of Multi-Engine Ocr Systems
University of Central Florida STARS Electronic Theses and Dissertations, 2004-2019 2004 Ocr: A Statistical Model Of Multi-engine Ocr Systems Mercedes Terre McDonald University of Central Florida Part of the Electrical and Computer Engineering Commons Find similar works at: https://stars.library.ucf.edu/etd University of Central Florida Libraries http://library.ucf.edu This Masters Thesis (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more information, please contact [email protected]. STARS Citation McDonald, Mercedes Terre, "Ocr: A Statistical Model Of Multi-engine Ocr Systems" (2004). Electronic Theses and Dissertations, 2004-2019. 38. https://stars.library.ucf.edu/etd/38 OCR: A STATISTICAL MODEL OF MULTI-ENGINE OCR SYSTEMS by MERCEDES TERRE ROGERS B.S. University of Central Florida, 2000 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in the Department of Electrical and Computer Engineering in the College of Engineering and Computer Science at the University of Central Florida Orlando, Florida Summer Term 2004 ABSTRACT This thesis is a benchmark performed on three commercial Optical Character Recognition (OCR) engines. The purpose of this benchmark is to characterize the performance of the OCR engines with emphasis on the correlation of errors between each engine. The benchmarks are performed for the evaluation of the effect of a multi-OCR system employing a voting scheme to increase overall recognition accuracy. This is desirable since currently OCR systems are still unable to recognize characters with 100% accuracy. -
Gradu04243.Pdf
Paperilomakkeesta tietomalliin Kalle Malin Tampereen yliopisto Tietojenkäsittelytieteiden laitos Tietojenkäsittelyoppi Pro gradu -tutkielma Ohjaaja: Erkki Mäkinen Toukokuu 2010 i Tampereen yliopisto Tietojenkäsittelytieteiden laitos Tietojenkäsittelyoppi Kalle Malin: Paperilomakkeesta tietomalliin Pro gradu -tutkielma, 61 sivua, 3 liitesivua Toukokuu 2010 Tässä tutkimuksessa käsitellään paperilomakkeiden digitalisointiin liittyvää kokonaisprosessia yleisellä tasolla. Prosessiin tutustutaan tarkastelemalla eri osa-alueiden toimintoja ja laitteita kokonaisjärjestelmän vaatimusten näkökul- masta. Tarkastelu aloitetaan paperilomakkeiden skannaamisesta ja lopetetaan kerättyjen tietojen tallentamiseen tietomalliin. Lisäksi luodaan silmäys markki- noilla oleviin valmisratkaisuihin, jotka sisältävät prosessin kannalta oleelliset toiminnot. Avainsanat ja -sanonnat: lomake, skannaus, lomakerakenne, lomakemalli, OCR, OFR, tietomalli. ii Lyhenteet ADRT = Adaptive Document Recoginition Technology API = Application Programming Interface BAG = Block Adjacency Graph DIR = Document Image Recognition dpi= Dots Per Inch ICR = Intelligent Character Recognition IFPS = Intelligent Forms Processing System IR = Information Retrieval IRM = Image and Records Management IWR = Intelligent Word Recognition NAS = Network Attached Storage OCR = Optical Character Recognition OFR = Optical Form Recognition OHR = Optical Handwriting Recognition OMR = Optical Mark Recognition PDF = Portable Document Format SAN = Storage Area Networks SDK = Software Development Kit SLM -
Journal of the Text Encoding Initiative, Issue 5 | June 2013, « TEI Infrastructures » [Online], Online Since 19 April 2013, Connection on 04 March 2020
Journal of the Text Encoding Initiative Issue 5 | June 2013 TEI Infrastructures Tobias Blanke and Laurent Romary (dir.) Electronic version URL: http://journals.openedition.org/jtei/772 DOI: 10.4000/jtei.772 ISSN: 2162-5603 Publisher TEI Consortium Electronic reference Tobias Blanke and Laurent Romary (dir.), Journal of the Text Encoding Initiative, Issue 5 | June 2013, « TEI Infrastructures » [Online], Online since 19 April 2013, connection on 04 March 2020. URL : http:// journals.openedition.org/jtei/772 ; DOI : https://doi.org/10.4000/jtei.772 This text was automatically generated on 4 March 2020. TEI Consortium 2011 (Creative Commons Attribution-NoDerivs 3.0 Unported License) 1 TABLE OF CONTENTS Editorial Introduction to the Fifth Issue Tobias Blanke and Laurent Romary PhiloLogic4: An Abstract TEI Query System Timothy Allen, Clovis Gladstone and Richard Whaling TAPAS: Building a TEI Publishing and Repository Service Julia Flanders and Scott Hamlin Islandora and TEI: Current and Emerging Applications/Approaches Kirsta Stapelfeldt and Donald Moses TEI and Project Bamboo Quinn Dombrowski and Seth Denbo TextGrid, TEXTvre, and DARIAH: Sustainability of Infrastructures for Textual Scholarship Mark Hedges, Heike Neuroth, Kathleen M. Smith, Tobias Blanke, Laurent Romary, Marc Küster and Malcolm Illingworth The Evolution of the Text Encoding Initiative: From Research Project to Research Infrastructure Lou Burnard Journal of the Text Encoding Initiative, Issue 5 | June 2013 2 Editorial Introduction to the Fifth Issue Tobias Blanke and Laurent Romary 1 In recent years, European governments and funders, universities and academic societies have increasingly discovered the digital humanities as a new and exciting field that promises new discoveries in humanities research. -
Operator's Guide Introduction
P3PC-2432-01ENZ0 Operator's Guide Introduction Thank you for purchasing our Color Image Scanner, ScanSnap S1500/S1500M (hereinafter referred to as "the ScanSnap"). This Operator's Guide describes how to handle and operate the ScanSnap. Before using the ScanSnap, be sure to read this manual, "Safety Precautions" and "Getting Started" thoroughly for proper operation. Microsoft, Windows, Windows Vista, PowerPoint, SharePoint, and Entourage are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. Word and Excel are the products of Microsoft Corporation in the United States. Apple, Apple logo, Mac, Mac OS, and iPhoto are trademarks of Apple Inc. Adobe, the Adobe logo, Acrobat, and Adobe Reader are either registered trademarks or trademarks of Adobe Systems Incorporated. Intel, Pentium, and Intel Core are either registered trademarks or trademarks of Intel Corporation in the United States and other countries. PowerPC is a trademark of International Business Machines Corporation in the United States, other countries, or both. Cardiris is a trademark of I.R.I.S. ScanSnap, ScanSnap logo, and CardMinder are the trademarks of PFU LIMITED. Other product names are the trademarks or registered trademarks of the respective companies. ABBYY™ FineReader™ 8.x Engine © ABBYY Software House 2006. OCR by ABBYY Software House. All rights reserved. ABBYY, FineReader are trademarks of ABBYY Software House. Manufacture PFU LIMITED International Sales Dept., Imaging Business Division, Products Group Solid Square East Tower, 580 Horikawa-cho, Saiwai-ku, Kawasaki-shi, Kanagawa 212-8563, Japan Phone: 044-540-4538 All Rights Reserved, Copyright © PFU LIMITED 2008. 2 Introduction Description of Each Manual When using the ScanSnap, read the following manuals as required. -
ABBYY® Finereader 14
ABBYY® FineReader 14 User’s Guide © 2017 ABBYY Production LLC. All rights reserved. ABBYY® FineReader 14 User’s Guide Information in this document is subject to change w ithout notice and does not bear any commitment on the part of ABBYY. The softw are described in this document is supplied under a license agreement. The softw are may only be used or copied in strict accordance w ith the terms of the agreement. It is a breach of the "On legal protection of softw are and databases" law of the Russian Federation and of international law to copy the softw are onto any medium unless specifically allow ed in the license agreement or nondisclosure agreements. No part of this document may be reproduced or transmitted in any from or by any means, electronic or other, for any purpose, w ithout the express w ritten permission of ABBYY. Copyrights 262 2 ABBYY® FineReader 14 User’s Guide Contents Introducing ABBYY FineReader ..................................................................................... 8 About ABBYY FineReader ........................................................................................... 9 What's New in ABBYY FineReader .............................................................................. 11 The New Task window ................................................................................................ 13 Viewing and editing PDFs ........................................................................................... 15 Quick conversion .................................................................................................... -
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. -
ABBYY Finereader 10 User’S Guide
ABBYY® FineReader Version 10 User’s Guide © 2009 ABBYY. All rights reserved. ABBYY FineReader 10 User’s Guide Information in this document is subject to change without notice and does not bear any commitment on the part of ABBYY. The software described in this document is supplied under a license agreement. The software may only be used or copied in strict accordance with the terms of the agreement. It is a breach of the "On legal protection of software and databases" law of the Russian Federation and of international law to copy the software onto any medium unless specifically allowed in the license agreement or nondisclosure agreements. No part of this document may be reproduced or transmitted in any from or by any means, electronic or other, for any purpose, without the express written permission of ABBYY. © 2009 ABBYY. All rights reserved. ABBYY, the ABBYY logo, ABBYY FineReader, ADRT are either registered trademarks or trademarks of ABBYY Software Ltd. © 1984-2008 Adobe Systems Incorporated and its licensors. All rights reserved. Adobe® PDF Library is licensed from Adobe Systems Incorporated. Adobe, Acrobat, the Adobe logo, the Acrobat logo, the Adobe PDF logo and Adobe PDF Library are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States and/or other countries. © 1996-2007 LizardTech, Inc. All rights reserved. DjVu is protected by U.S. Patent No. 6.058.214. Foreign Patents Pending. Fonts Newton, Pragmatica, Courier © 2001 ParaType, Inc. Font OCR-v-GOST © 2003 ParaType, Inc. © 2007 Microsoft Corporation. All rights reserved. Microsoft, Outlook, Excel, PowerPoint, Visio, Windows Vista, Windows are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. -
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 .................................................................................................. -
Ocrocis Tutorial
Ocrocis A high accuracy OCR method to convert early printings into digital text A Tutorial Uwe Springmann Center for Information and Language Processing (CIS) Ludwig-Maximilians-University, Munich Email: springmann (at) cis.lmu.de version history: version 0.95: 05. March 2015 version 0.96: 11. August 2015 This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0. Ocrocis v. 0.96 2 Contents 1 Abstract 2 2 Introduction 3 3 The OCR workflow 8 3.1 Image acquisition ............................... 8 3.2 Preprocessing ................................. 8 3.3 Training ..................................... 10 3.3.1 Annotation ............................... 10 3.3.2 Training a model ............................ 13 3.4 Character recognition ............................. 16 3.5 Postprocessing ................................. 16 4 A practical example 16 4.1 Download the images ............................. 17 4.2 Binarize the images .............................. 17 4.3 Segment into lines ............................... 18 4.4 Choose images to annotate .......................... 18 4.5 Train a model .................................. 19 4.6 Test the models ................................. 20 4.7 Recognize the book .............................. 23 4.8 Extract the text ................................. 23 5 Demo data 24 6 Overview of the command sequence 24 7 References 24 1 Abstract This tutorial describes the first successful application of OCR to convert scanned images of books over the complete history of modern printing since Gutenberg into highly accu- rate digital text (above 95% correctly recognized characters for even the earliest books with good scans). This opens up the possibility of transforming our textual culturage heritage by OCR methods into electronic text in a much faster and cheaper way than by manual transcription.