Extracción Automáticas De Modelos Uml Contenidos En Imágenes
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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. -
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. -
Glosar De Termeni
CUPRINS 1. Reprezentarea digitală a informaţiei……………………………………………………….8 1.1. Reducerea datelor………………………………………………………………..10 1.2. Entropia şi câştigul informaţional………………………………………………. 10 2. Identificarea şi analiza tipurilor formatelor de documente pe suport electronic……... 12 2.1. Tipuri de formate pentru documentele pe suport electronic……………………..…13 2.1.1 Formate text……………………………………………………………… 13 2.1.2 Formate imagine……………………...........……………………………....26 2.1.3 Formate audio……………………………………………………………...43 2.1.4 Formate video……………………………………………………………. 48 2.1.5. Formate multimedia.....................................................................................55 2.2. Comparaţie între formate………………………….………………………………72 3. Conţinutul web………………………………………………………………………………74 3.1. Identificarea formatelor de documente pentru conţinutul web……………………. 74 4. Conversia documentelor din format tradiţional în format electronic…………………101 4.1. Metode de digitizare……………………………………………………………..104 4.1.1 Cerinţe în procesul de digitizare……………………………………………104 4.1.2. Scanarea……………………………………………………………………104 4.1.3. Fotografiere digitală………………………………………………………105 4.1.4. Scanarea fotografiilor analogice………………………………………106 4.2. Arhitectura unui sistem de digitizare………………………………………………106 4.3. Moduri de livrare a conţinutului digital………………………………………….108 4.3.1. Text sub imagine………….……………………………………………..108 4.3.2. Text peste imagine…………………………………………….…………108 4.4. Alegerea unui format………………………………………………………………108 4.5. Tehnici de conversie prin Recunoaşterea Optică a Caracterelor (Optical Charater Recognition-OCR)……………………………………………109 4.5.1. Scurt istoric al conversiei documentelor din format tradiţional prin scanare şi OCR-izare…………………... …..109 4.5.2. Starea actuală a tehnologiei OCR……………………………………….110 4.5.3. Tehnologii curente, produse software pentru OCR-izare………………112 4.5.4. Suportul lingvistic al produselor pentru OCR-izare………………….…114 1 4.5.5. Particularităţi ale sistemelor OCR actuale………………………..……..118 5. Elaborarea unui model conceptual pentru un sistem de informare şi documentare………………………………………………………126 5.1. -
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 -
Download Resume
[email protected] +919635337441 Bibhash Chandra Mitra A dynamic professional, targeting challenging & rewarding opportunities in Machine Learning and Artificial Intelligence with an organization of high repute and big challenges preferably in Pune, Bangalore or Kolkata. https://www.linkedin.com/in/bibhashmitra220896 https://github.com/Bibyutatsu https://bibyutatsu.github.io https://bibyutatsu.github.io/Blogs Profile Summary A focused and goal-oriented professional with 1+ year of industrial exposure in Data Science, Machine Learning-supervised/unsupervised, Artificial Intelligence and Algorithms Alumni of IIT, Kharagpur ; graduated with a Major in Aerospace Engineering and a Minor in Computer Science Engineering Currently associated with Innoplexus Consulting Services Pvt. Ltd.; working on critical projects likeNovel Drug Discovery with AI and Cluster- ing Graph Networks for Entity Normalisation Received rating of 5/5 employee for “Outstanding” performance for two quarters (Jun’19 to Dec’19) Expertise in OCR Engines, Deep Learning, Data Exploration and Visualization, Predictive Modelling and Optimization Proficiency in using various AI techniques such as RCNN, VAE, GAN and RL Worked on projects such as ‘Table Detection and Extraction using FRCNN and Image processing’ & ‘Hierarchy using Graphs Hands-on experience in Docker, Virtual Environments, Anaconda, DGX-1, Tesla V100 Modeling: Designing and implementing statistical/predictive models and cutting edge algorithms by utilizing diverse sources of data to predict demand, -
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 -
Insight MFR By
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Operational Platform with Additional Tools Integrated
DELIVERABLE SUBMISSION SHEET To: Cristina Maier (Project Officer) DG CONNECT G.2 Creativity Unit EUFO 01/162 - European Commission L-2557 Luxembourg From: Support Action Centre of Competence in Digitisation Project acronym: Succeed Project Number: 600555 Project Manager: Rafael C. Carrasco Jiménez Project Coordinator: Universidad de Alicante The following deliverable: Deliverable title: Operational platform with additional tools integrated Deliverable number: D2.4 Deliverable date: M23 Partners responsible: UA, KB Status: X Public Restricted Confidential is now complete X It is available for your inspection X Relevant descriptive documents are attached The deliverable is: X A document X A website Url: http://succeed-project.eu/demonstrator- platform X Software An event Other Sent to Project Officer: Sent to functional mailbox: On date: [email protected] [email protected] 04/12/2014 Succeed is supported by the European Union under FP7-ICT and coordinated by Universidad de Alicante. D2.4 Operational platform with additional tools integrated Support Action Centre of Competence in Digitisation (Succeed) December 4, 2014 Abstract Deliverable 2.4 (Operational platform with additional tools integrated) is an online service and open-source platform which complements and enhances deliverable D2.3 (Operational platform, submitted in Decem- ber 2013 and approved in February 2014). This report summarises the digitisation tools incorporated into the online platform, as well as the management of the releases of its open-source components. In 2014, 13 tools have been integrated in addition to those initially available in the operational platform or subsequently added during Suc- ceed's first year. The operational platform, currently provides 41 tools for text digitisation which can be executed and tested online without the need to install any software on the user's computer. -
OCR with Printed Text Author: Gerry Kennedy © ICT Consultancy
Fact Sheet: Universal Access using OCR with Printed Text Author: Gerry Kennedy © ICT Consultancy Title: Universal Access using OCR with Printed Text Author: Gerry Kennedy © February 2009 Software: OCR (Optical Character Recognition) programs Category: Scanning devices OCR applications and access to text 1. Introduction Optical Character Recognition (usually referred to simply as OCR) software has been used for many years in education, research and business. It provides access to text in all manner of printed materials. OCR software makes it easy to re-use and access existing printed or online documents with perfect formatting. Converted documents can look exactly like the original – complete with colour, columns, tables, bullets and graphics. Optical character recognition „is the mechanical or electronic translation of images of handwritten, typewritten or printed text (usually captured by a scanner) into machine-editable text.‟ [http://en.wikipedia.org/wiki/Optical_character_recognition] Any scanning device can benefit from the power of unlocking and sharing information stored on paper as accurately reproduced digital documents. They can be reversioned as PDF, MS Word, HTML documents or purely as text. Various OCR programs provide the speed, accuracy, quality and features to maximise the benefits of current scanning or all-in-one devices. Note: The device needs to be OCR capable or can save/convert a file in a format that can be used by scanning software. With most flatbed scanners, photocopiers or printer-scanner models, converting information that was formerly „locked‟ on paper or in digital files into a preferred format, is made simpler and easier than previously imagined. Just load the print material, convert it and students are ready to re-use it. -
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 .................................................................................................. -
Tool for Forensic Analyses of Digital Traces
Masaryk University Faculty of Informatics Tool for forensic analyses of digital traces Master’s Thesis Bc. Mária Hatalová Brno, Spring 2018 Declaration Hereby I declare that this paper is my original authorial work, which I have worked out on my own. All sources, references, and literature used or excerpted during elaboration of this work are properly cited and listed in complete reference to the due source. Bc. Mária Hatalová Advisor: prof. RNDr. Václav Matyáš M.Sc. Ph.D. i Acknowledgements I would like to express my gratitude to my advisor prof. Václav Matyáš for the continuous guidance throughout writing the thesis, for his insightful comments, and for enhancing my motivation. My thanks also go to my technical consultant Mgr. Roman Pavlík from Trusted Network Solutions, a.s. for providing me an insight into the work of expert witnesses in the field of IT, for his encourage- ment, and for giving me the perception that the work is meaningful and useful in practice. I would also like to thank Petr Svoboda from Trusted Network Solu- tions, a.s. for consultations regarding IT Forensic Tool v1.0 and for tech- nical support. Last but not least, my gratitude goes to my family and friends for their support and patience. ii Abstract The thesis examines selected issuesin the area of forensic analy- sis and their impact on expert witnesses that work for the Police of the Czech Republic. A description of a typical forensic task as it is assigned by the police is provided, together with an overview of avail- able tools that could be used for solving the task.