DOCSLIB.ORG

Download Download

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Download Download -1- Which OCR toolset is good and why? A comparative study Pooja Jain1,*, Dr. Kavita Taneja1, Dr. Harmunish Taneja2 1Dept. Of Computer Science & Applications, Panjab University, Chandigarh, India. 2Dept. Of Computer Science & Information Tech., DAV College, Sec - 10, Chandigarh, India. *Corresponding author: [email protected] Abstract Optical Character Recognition (OCR) is a very active research area in many challenging fields like pattern recognition, natural language processing (NLP), computer vision, biomedical informatics, machine learning (ML), and artificial intelligence (AI). This computational technology extracts the text in an editable format (MS Word/Excel, text files, etc.) from PDF files, scanned or hand-written documents, images (photographs, advertisements, and alike), etc. for further processing and has been utilized in many real-world applications including banking, education, insurance, finance, healthcare and keyword-based search in documents, etc. Many OCR toolsets are available under various categories, including open-source, proprietary, and online services. This research paper provides a comparative study of various OCR toolsets considering a variety of parameters. Keywords: ABBYY FineReader; Calamari; Google Docs; OCR; Tesseract. 1. Introduction OCR (Bokser, 1992; Mori et al., 1992) is a 1.1. OCR process commonly used technology for recognizing text within digital images such as OCR process (Goswami et al., 2013; Cao, scanned documents, advertisements, 2014; Tomaschek, 2018) generally goes photographs, etc. It is widely used as an through multiple stages, as shown in Figure information entry tool that can extract useful 1, including Image-acquisition (downloading information from scanned documents, image from an online source or capturing it including printed forms (filled by users), using a camera or scanner), Pre-processing computerized receipts, bank statements, (modifying image in a way that may increase invoices, business cards, passport documents, the accuracy of OCR), Binarization mails, or any other suitable documentation. (separating the content from the background), Other applications include searching within Layout Analysis (a division of the document institutional repositories and scanned legal into various homogeneous regions), Character documents, automatic number plate level segmentation (segmentation of the recognition (ANPR), processing cheques in image into lines, words, and characters), banks, recognizing barcodes, testing text- Recognition (feature extraction of every based captcha codes, etc. character image) and finally, Classification (determining the output characters) followed by Postprocessing where classification results can be enhanced using various language models and dictionaries. Kuwait Journal of Science -2- Fig. 1. OCR Process 1.2. OCR Challenges OCR faces many problems in recognizing Fig. 2. Popular OCR Softwares. printed or handwritten characters such as image deformation (disconnected line domain. The rest of the paper is arranged as segments, isolated dots, breaks or holes in follows. First proprietary OCR toolsets are lines, rotation of text, etc.), shape introduced, followed by open-source and discrimination (some characters have very online OCR toolsets. Then a literature similar shapes like 0 (zero) and O, 5 and S, review for various comparative studies 2 and Z, 6 and G, etc.), size and pitch conducted for OCR toolsets is presented, variations, cluttered background, a camera followed by the discussion and conclusion captured documents (motion and out-of- of the study. focus blur), multilingual documents, text 2. Proprietary OCR Toolsets formatting, and complicated structures and natural scene text (variation in illumination Proprietary OCR toolsets are usually paid conditions and fonts, etc.). Touching for and supported by developers. They characters and different typefaces used in generally have an excellent graphical user early printed books (15th-19th century) interface (GUI). Popular proprietary OCR pose additional text recognition difficulties. toolsets are discussed in this section, and Recognition of calligraphy typefaces and their important features are summarized in fonts is another challenge (Al-Hmouz, Table 1. 2020). 2.1. ABBYY FineReader 1.3. OCR Toolsets ABBYY FineReader achieves up to 100 % OCR toolsets are software that focus on word-level accuracy with high quality accurate character recognition in a reliable images in English language manner. OCR toolsets can be broadly (https://abbyy.technology/_media/en:produ classified into three categories: cts:fre:win:v11:frengine11_performance_g (i) Proprietary uide.pdf), and has been an undisputed (ii) Open Source choice for layout analysis and OCR. It can (iii) Online be accessed in two different ways: ABBYY FineReader SDK and ABBYY Online/ Figure 2 presents various popular OCR cloud service. toolsets in different categories. Since many proprietaries, open-source, and online OCR 2.2. Transym OCR (TOCR) toolsets with varied features are available to (http://www.transym.com/tocr-the- choose from, this research paper reviews integrators-choice.htm) and analyse the performance of various OCR toolsets and provide better insight to TOCR (Tafti et al., 2016) is specifically the OCR study with an aim to help designed keeping in mind the ease of researchers in making a right choice of an integration with other softwares. With a OCR toolset specific to their application very light and efficient GUI, it is claimed Kuwait Journal of Science -3- that Transym has the capability to read French, and Spanish). For other languages, blurred, obscure, and even broken their language pack must be installed first. characters (Vithlani et al., 2015). However, multiple languages cannot be set 2.3. Readiris for a single document in MODI. (https://www.irislink.com/EN- 3. Open-source OCR Toolsets US/c1729/Readiris-17--the-PDF-and- Open-source OCR engines are best OCR-solution-for-Windows-.aspx) controlled via their command-line Preserving the original page layout, interfaces, and most of them do not have a Readiris automatically converts text from GUI. Some of the popular open-source PDF files, images, or paper documents into OCR toolsets are discussed in this section, fully editable files. Being compatible with and their important features are summarized most of the scanners in the market, it in Table 2. supports many different input formats and 3.1. Tesseract provides an attractive and intuitive GUI. 2.4. Adobe Acrobat Tesseract (Smith, 2007; Patel et al., 2012) (https://acrobat.adobe.com/in/en/acrobat/h comes with an easy-to-use command-line ow-to/ocr-software-convert-pdf-to- tool called ‘tesseract.’ It can be integrated in text.html) C++ or Python code by using Tesseract’s API. Many GUI desktop applications use Adobe Acrobat automatically converts Tesseract as a text recognition engine, image files, scanned documents, and PDF including FreeOCR, OCRFeeder, PDF files into searchable/editable documents OCR X, QTesseract, YAGF, while preserving the format and its accuracy gImageReader, Lector, VietOCR, is reportedly high. It provides fewer SunnyPage, and Lime OCR. Also, WeOCR, language options comparing with ABBYY CustomOCR, i2OCR, and NewOCR are FineReader, but it is more pervasive web applications using the Tesseract tool software as it is more business-oriented and (Vithlani et al., 2015). Its latest version, less academic. Tesseract4.0 (https://github.com/tesseract- 2.5. OmniPage ocr/tesseract/wiki/4.0-with-LSTM), uses a kind of Recurrent Neural Network (RNN) (https://www.kofax.com/Products/omnipage) called Long Short Term Memory (LSTM) OmniPage((https://www.nuancesoftwarest based recognition engine. ore.com/omnipage-ultimate) claims 99% or more character-accuracy and converts paper 3.2. Ocrad documents and PDFs into fully editable (https://www.gnu.org/software/ocrad/manu al/ocrad_manual.html) digital files preserving all type of formatting. Using OmniPage, digital Ocrad supports narrowing down the camera photos and other images can also be character search by defining which converted into text files. character sets to recognize. Ocrad recognizes characters very fast, but at the 2.6. Microsoft Office Document Imaging same time, it is very sensitive to character (MODI) defects, and it is difficult to modify Ocrad (https://support.microsoft.com/da- to recognize new characters. Best character dk/help/982760/install-modi-for-use-with- recognition results are achieved when microsoft-office-2010) characters are at least 20 pixels high, or the MODI is a free OCR tool that can scan hard image is scanned at 300 dpi. copies of documents and import them to MS Word for editing. By default, MODI can perform OCR in three languages (English, Kuwait Journal of Science -4- 3.3. GOCR 3.6. Calamari (http://jocr.sourceforge.net/) (https://github.com/Calamari-OCR) GOCR can be used either as a stand-alone Calamari (Reul et al., 2019) is one of the console application or as a back-end (OCR latest open-source OCR line recognition tool. engine) to other programs. Written in C Based on state-of-the-art Deep Neural language, its recognition process takes two Networks (DNNs) implemented in passes. In the first pass, the entire document Tensorflow (including Convolutional Neural is called. In the second pass, the unknown Networks (CNN) and LSTM layers), it uses characters are called (Dhiman et al., 2013). techniques such as pretraining and voting, It is claimed that GOCR can handle which help in minimizing its character error single-column sans-serif
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.....................................................................................................................
    [Show full text]