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Automatic identification and data capture

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Automatic identification and data capture

  1. 1. Automatic identification and data captureFrom Wikipedia, the free encyclopediaJump to: navigation, searchThis article needs additional citations for verification. Please help improve this articleby adding citations to reliable sources. Unsourced material may be challenged andremoved. (June 2011)Automatic identification and data capture (AIDC) refers to the methods of automaticallyidentifying objects, collecting data about them, and entering that data directly into computersystems (i.e. without human involvement). Technologies typically considered as part of AIDCinclude bar codes, Radio Frequency Identification (RFID), biometrics, magnetic stripes, OpticalCharacter Recognition (OCR), smart cards, and voice recognition. AIDC is also commonlyreferred to as “Automatic Identification,” “Auto-ID,” and "Automatic Data Capture."AIDC is the process or means of obtaining external data, particularly through analysis of images,sounds or videos. To capture data, a transducer is employed which converts the actual image or asound into a digital file. The file is then stored and at a later time it can be analyzed by acomputer, or compared with other files in a database to verify identity or to provide authorizationto enter a secured system. Capturing of data can be done in various ways; the best methoddepends on application.AIDC also refers to the methods of recognizing objects, getting information about them andentering that data or feeding it directly into computer systems without any human involvement.Automatic identification and data capture technologies include barcodes, RFID, bokodes, OCR,magnetic stripes, smart cards and biometrics (like iris and facial recognition system).In biometric security systems, capture is the acquisition of or the process of acquiring andidentifying characteristics such as finger image, palm image, facial image, iris print or voiceprint which involves audio data and the rest all involves video data.Radio frequency identification (RFID) is relatively a new AIDC technology which was firstdeveloped in 1980’s. The technology acts as a base in automated data collection, identificationand analysis systems worldwide. RFID has found its importance in a wide range of marketsincluding livestock identification and Automated Vehicle Identification (AVI) systems becauseof its capability to track moving objects. These automated wireless AIDC systems are effectivein manufacturing environments where barcode labels could not survive.Contents1 Capturing data from printed documents2 The Internet and the future3 AIDC 1004 See also
  2. 2. 5 ReferencesCapturing data from printed documentsThis section appears to be written like an advertisement. Please help improve it byrewriting promotional content from a neutral point of view and removing anyinappropriate external links. (February 2012)One of the most useful application tasks of data capture is collecting information from paperdocuments and saving it into databases (CMS, ECM and other systems). There are several typesof basic technologies used for data capture according to the data type:[citation needed]OCR – for printed text recognition[citation needed]ICR – for hand-printed text recognition[citation needed]OMR – for marks recognition[citation needed]OBR – for barcodes recognition[citation needed]BCR – for business cards recognition[citation needed]DLR - for document layer recognition[citation needed]These basic technologies allow extracting information from paper documents for furtherprocessing it in the enterprise information systems such as ERP, CRM and others.[citation needed]The documents for data capture can be divided into 3 groups: structured, semi-structured andunstructured.[citation needed]Structured documents (questionnaires, tests, insurance forms, tax returns, ballots, etc.) havecompletely the same structure and appearance. It is the easiest type for data capture, becauseevery data field is located at the same place for all documents.[citation needed]Semi-structured documents (invoices, purchase orders, waybills, etc.) have the same structurebut their appearance depends on number of items and other parameters. Capturing data fromthese documents is a complex, but solvable task.[citation needed]Unstructured documents (letters, contracts, articles, etc.) could be flexible with structure andappearance.DeveloperBasicTechnologiesData Capture Application Data Capture SDKABBYYOCR (195languages),ICR (113languages),OMR, OBR,ABBYY FlexiCapture is anintelligent data anddocument capture softwarethat delivers automatedprocessing of any type ofABBYY FlexiCapture Engine isa data and document captureSDK for any type ofstructured, semi-structuredand unstructured documents
  3. 3. BCR structured, semi-structured andunstructured documentsand formsand formsAccusoftOCR (118languages),ICR (11languages),OMR, OBRImageGear for .NET is anSDK that delivers fullymanaged code for WinForms,ASP.NET, and WPF applicationdevelopment. OptionalRecognition component enablesa comprehensive integratedOCR toolkit.FormSuite, available for .NETor ActiveX, is a structuredforms processing SDK designedto handle forms processing fromscanning to recognition.Barcode recognition andcreation can also be added.AnyDocSoftwareOCR (4languages),ICR, OMR,OBROCR for AnyDoc automatesdata capture from allbusiness documents,including structured, semi-structured, andunstructured documentsby incorporating AnyAppTechnology for template-free processing.CvisionTechnologiesOCR (60languages),ICR (60languages),OMR, OBRCvisions Trapeze is anintelligent software that isable to recognize andcapture text fromstructured, semi-structured, andunstructured documentsincluding forms, invoices,and EOBsCvisions Trapezes SDKcaptures data from structured,semi-structured, andunstructured documentsincluding forms, invoices, andEOBsExpervisionOCR (18languages),ICR (18languages),OMR, OBR,BCRExpervision TypeReaderExpervision TypeReader canautomatically process fulltext documents. In under thepremise of accuratelyidentification, its processingspeed can reach above 100Expervision OpenRTK Engine isan intelligent capture data anddocument processing SDK. Ithas flexible language supportfunction, in theory, it cansupport additional anyonelanguage and train the engine to
  4. 4. pages each minute. adapt various fonts according tocustomize demand. CustomizedAPI definition and developmentare supported.I.R.I.S. GroupOCR (120languages),ICR (Latinbasedlanguages),OMR, OBR,BCRIRISCapture for Invoices –invoice processing solutionIRISCapture Pro for Formsis an intelligent softwaresuite that automaticallycaptures, sorts and identifiesall types of documents andformsLEADTOOLSOCR (118languages),ICR (15languages),OMR, OBR,BCRLEADTOOLS FormsRecognition module is a .NETSDK that harnesses the power ofLEADs image processingtechnology to intelligentlyidentify form components andfeatures that can be used torecognize structured formsNuanceCommunicationsOCR (120languages),ICR, OMR,OBR, BCROmniPage Professional 17makes structured formsmade easy from start tofinish. You can turn paperforms into electronic formsand then collect the data.OmniPage Capture SDK forWindows with its advancedLogical Form Recognition(LFR) automates form templatecreation and structured formsprocessing.PSIGENSoftwareOCR (99languages),ICR, OMR,OBR, BCR(1D and 2D)PSI:Capture is a completecapture solution thatincludes all the functionlityrequired to automaticallyprocess all structured andsemi-structured documents,including invoices, formsand general mail. One of itskey strengths is itsunrivalled dynamic interfaceto SharePoint.The Internet and the futureThe idea is as simple as its application is difficult. If all cans, books, shoes or parts of cars areequipped with minuscule identifying devices, daily life on our planet will undergo atransformation. Things like running out of stock or wasted products will no longer exist as we
  5. 5. will know exactly what is being consumed on the other side of the globe. Theft will be a thing ofthe past as we will know where a product is at all times. Counterfeiting of critical or costly itemssuch as drugs, repair parts, or electronic components will be reduced or eliminated becausemanufacturers or other supply chain entities will know where their products are at all times.Product wastage or spoilage will be reduced because environmental sensors will alert suppliersor consumers when sensitive products are exposed to excessive heat, cold, vibration, or otherrisks. Supply chains will operate far more efficiently because suppliers will ship only theproducts needed when and where they are needed. Consumer and supplier prices should alsodrop accordingly.[1]The global association Auto-ID Center was founded in 1999 and is made up of 100 of the largestcompanies in the world such as Wal-Mart, Coca-Cola, Gillette, Johnson & Johnson, Pfizer,Procter & Gamble, Unilever, UPS, companies working in the sector of technology such as SAP,Aliens, Sun as well as five academic research centers.[2]These are based at the followingUniversities; MIT in the USA, Cambridge University in the UK, the University of Adelaide inAustralia, Keio University in Japan and University of St. Gallen in Switzerland.The Auto-ID Center suggests a concept of a future supply chain that is based on the Internet ofobjects, i.e. a global application of RFID. They try to harmonize technology, processes andorganization. Research is focused on miniaturization (aiming for a size of 0.3 mm/chip),reduction in the price per single device (aiming at around $0.05 per unit), the development ofinnovative application such as payment without any physical contact (Sony/Philips), domotics(clothes equipped with radio tags and intelligent washing machines) and, last but not least,sporting events (timing at the Berlin marathon).AIDC 100AIDC 100 is a professional organization for the automatic identification and data capture(AIDC) industry. This group is composed of individuals who made substantial contributions tothe advancement of the industry. Increasing businesss understanding of AIDC processes andtechnologies are the primary goals of the organization.[3]See alsoAuto-ID LabsDevice managementField Service ManagementMobile EnterpriseMobile Asset ManagementUbiquitous computingUbiquitous CommerceDigital MailroomReferences
  6. 6. 1. ^ Waldner, Jean-Baptiste (2008). Nanocomputers and Swarm Intelligence. London: ISTEJohn Wiley & Sons. pp. 205–214. ISBN 1-84704-002-0.2. ^ Auto-ID Center. "The New Network". Retrieved 23 June 2011.3. ^ "AIDC 100". AIDC 100: Professionals Who Excel in Serving the AIDC Industry.Archived from the original on 24 July 2011. Retrieved 2 August 2011.Categories:Automatic identification and data captureEncodingsMultimodal interactionHuman–computer interactionRadio-frequency identificationNavigation menuCreate accountLog inArticleTalkReadEditView historyNavigationMain pageContentsFeatured contentCurrent eventsRandom articleDonate to WikipediaInteractionHelpAbout WikipediaCommunity portalRecent changesContact Wikipedia
  7. 7. ToolboxWhat links hereRelated changesUpload fileSpecial pagesPermanent linkPage informationCite this pagePrint/exportCreate a bookDownload as PDFPrintable versionLanguagesDeutschItalianoPortuguêsРусскийSvenskaTürkçe中文Edit linksThis page was last modified on 17 April 2013 at 11:50.Text is available under the Creative Commons Attribution-ShareAlike License;additional terms may apply. By using this site, you agree to the Terms of Use and PrivacyPolicy.Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profitorganization.

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