OCR Pwds and Assistive Qatari Using OCR Issue No
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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. -
Video-Based Tracking of Physical Documents on a Desk
FACULTY OF ENGINEERING Department of Electronics and Informatics Video-based Tracking of Physical Documents on a Desk Graduation thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Applied Sciences and Engineering: Applied Computer Science Sone Nsime Ngole Promoter: Prof. Dr. Beat Signer Advisor: Dr. Bruno Dumas JANUARY 2014 FACULTEIT INGENIEURSWETENSCHAPPEN Vakgroep Elektronica en Informatica Video-based Tracking of Physical Documents on a Desk Afstudeer eindwerk ingediend in gedeeltelijke vervulling van de eisen voor het behalen van de graad Master of Science in de Ingenieurswetenschappen: Toegepaste Computerwetenschappen Sone Nsime Ngole Promoter: Prof. Dr. Beat Signer Advisors: Dr. Bruno Dumas JANUARI 2014 Abstract Currently, physical and digital documents tend to stay in their world, without any direct relationship linking them. How- ever, a lot of physical documents are printed from digital doc- uments and conversely, digital documents can be scanned ver- sions of printed papers. Furthermore, the organization of piles of physical documents on a desk hints at shared semantic fea- tures between a set of documents. This thesis explores an ap- proach to link or re-link physical documents with their digital counterpart. This integration will be done by designing a sys- tem that uses an overhead digital camera to recognize, identify, localize and track paper documents on the physical desk space in real time (or offline by means of a pre-recorded video stream) and automatically matching them against an image database of electronic documents. The system locates each paper docu- ment that is present on the desk and reconstructs a complete configuration of documents on the desk at each instant in time. -
Diplomová Práce Inteligentní Vyhledávání Dokumentů
Západočeská univerzita v Plzni Fakulta aplikovaných věd Katedra informatiky a výpočetní techniky Diplomová práce Inteligentní vyhledávání dokumentů Plzeň 2017 Jiří Martínek Místo této strany bude zadání práce. Prohlášení Prohlašuji, že jsem diplomovou práci vypracoval samostatně a výhradně s použitím citovaných pramenů. V Plzni dne 16. května 2017 Jiří Martínek Poděkování Na tomto místě bych chtěl poděkovat svému vedoucímu diplomové práce doc. Ing. Pavlu Královi Ph.D. za odborné vedení, za pomoc a rady při zpracování této práce. Jiří Martínek Abstract This diploma thesis deals with information retrieval in a set of scanned documents in form of raster images. First, the images are converted into the text form using optical character recognition (OCR) methods. Unfortunately, there are errors in conversion, therefore another part of the work deals with error correction. This thesis propose several error correction methods that are combined to achieve the best possible results. Then, the corrected documents are indexed into the full-text Apache Solr database. The resulting application allows to efficiently find the requested document according to a full-text query. Error correction of the OCR output helps to increase the accuracy of full-text search. The accuracy of the system was experimentally verified on the real data. Abstrakt Tato diplomová práce se zabývá problematikou vyhledávání informací v množině naskenovaných dokumentů v podobě rastrových obrázků. Nejdříve je proto proveden převod rastrového obrázku do textové podoby pomocí metod optického rozpoznávání znaků (OCR). V rámci převodu bohužel dochází k chybám, proto se další část práce zabývá samotnou opravou chyb. V práci je navrženo několik metod oprav chyb, které jsou zkombinovány pro dosažení co nejlepšího výsledku. -
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. -
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. -
Desenvolvimento De Um Sistema De Apoio Ao Arquivo E À Gestão De
Diogo Alexandre Nascimento Alves Licenciatura em Ciências da Engenharia Electrotécnica e de Computadores [Nome completo do autor] Desenvolvimento de um Sistema de Apoio ao Arquivo e à [Habilitações Académicas]Gestão de Blocos Histológicos [Nome completo do autor] [Habilitações Académicas] [Nome[Título completo da Tese] do autor] Dissertação para obtenção do Grau de Mestre em [Habilitações Académicas] Engenharia Electrotécnica e de Computadores [Nome completo do autor] Orientador: José Manuel Matos Ribeiro da Fonseca, Professor Auxiliar com Dissertação para obtenção do Grau de Mestre em [Habilitações Agregação,Académicas] Faculdade de Ciências e Tecnologia da Universidade [Engenharia Informática] Nova de Lisboa Co-orientador[Nome completo: André do Teixeira autor] Bento Damas Mora, Professor Auxiliar, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa [Habilitações Académicas] [Nome completo do autor] [Habilitações Académicas] [Nome completo do autor] [Habilitações Académicas] i Setembro, 2018 Desenvolvimento de um Sistema de Apoio ao Arquivo e á Gestão de Blocos Histológicos Copyright © Diogo Alexandre Nascimento Alves, Faculdade de Ciências e Tecnologia, Univer- sidade Nova de Lisboa. A Faculdade de Ciências e Tecnologia e a Universidade Nova de Lisboa têm o direito, perpétuo e sem limites geográficos, de arquivar e publicar esta dissertação através de exemplares impres- sos reproduzidos em papel ou de forma digital, ou por qualquer outro meio conhecido ou que venha a ser inventado, e de a divulgar através de repositórios -
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. -
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 -
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.