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# Data encoder. A code is a set of symbols or signals that usually represent alphanumeric characters. When data are encoded, the characters are translated into machine-readable code. A label or tag containing the encoded data is attached to the item that is to be identified.
# Machine reader or scanner. This device reads the encoded data, converting them to an alternative form, typically an electrical analog signal.
# Data decoder. This component transforms the electrical signal into digital data and finally back into the original alphanumeric characters.
==Capturing data from printed documents==
One of the most usefulcommon application tasksapplications of data capture is collectingextracting information from paper documents and saving it into databases (CMS, ECM, and other systemsetc.). There are several types of basicBasic technologies used for data capture according tovary theby data type:{{Citation needed|date=April 2013}}
* [[Optical character recognition|OCR]] – for printed text recognition<ref>{{cite web |date=22 July 2016 |title=What is Optical Character Recognition (OCR)? |url=http://www.ukdataentry.com/optical-character-recognition/ |access-date=22 July 2016 |website=www.ukdataentry.com}}</ref><ref>{{Cite web|title=What is OCR? - Optical Character Recognition Explained|url=https://aws.amazon.com/what-is/ocr/|access-date=222025-06-27|website=Amazon JulyWeb 2016Services}}</ref><ref>{{Cite web|title=OCR - How it works|url=https://pdfguru.com/pdf-ocr|archive-url=https://archive.today/20130411134627/http://www.nicomsoft.com/optical-character-recognition-ocr-how-it-works/|archive-date=20162013-0704-2211|access-date=2025-06-27|website=PDFsoft}}</ref>
* [[Intelligent character recognition|ICR]] – for hand-printed text recognition<ref>{{CitationCite neededweb|title=ICR - Glossary |url=https://www.digitizationguidelines.gov/term.php?term=icr|access-date=April2025-06-27|website=Federal Agencies Digitization Guidelines 2013Initiative}}</ref>
* [[Optical mark recognition|OMR]] – for marks recognition<ref>Palmer, Roger C. (1989, Sept) The Basics of Automatic Identification [Electronic version]. Canadian Datasystems, 21 (9), 30-33</ref>
* OBR/BCR – for barcodesbarcode recognition<ref>{{cite news |url=http://searchmanufacturingerp.techtarget.com/definition/bar-code |title=bar code (or barcode) |publisher=TechTarget |date=2009-10-01 |access-date=2017-03-09 |first=Margaret |last=Rouse |archive-date=20172025-0806-10 27|archive-url=https://archive.today/20250627113601/https://web.archive.org/web/20170810075044/http://searchmanufacturingerp.techtarget.com/definition/bar-code |url-status=dead }}</ref>
* DLR – for document layer recognition{{Citation needed|date=April 2013}} ▼*BCR – for bar code recognition<ref>{{cite web|last1=Technologies|first1=Recogniform|title=Optical recognition and data-capture|url=http://www.recogniform.com/|website=www.recogniform.com|access-date=2015-01-15}}</ref>
▲*DLR – for document layer recognition{{Citation needed|date=April 2013}}
These basic technologies allowenable extractingdata informationextraction from paper documents for further processing in the enterprise information systems such aslike [[Enterprise resource planning|ERP]], and [[Customer relationship management|CRM]], and others.{{Citation needed|date=April 2013}}
The documents for data capture can be divided into 3 groups: '''structured''', '''semi-structured,''' '''and [[Unstructured data|unstructured]]'''.{{Citation needed|date=April 2013}}
'''Structured documents''' (e.g., questionnaires, tests, insurancetax formsreturns, taxinsurance returnsforms, ballots, etc.) have completelyidentical the same structure and appearance. It is the easiest typelayouts, formaking data capture becausestraightforward everysince data fieldfields isare locatedalways atin the same place___location.<ref for all documents.name=":0">{{CitationCite neededweb|title=Document Understanding - Document types|url=https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/document-types|access-date=April 20132025-06-27|website=docs.uipath.com}}</ref>
'''Semi-structured documents''' (e.g., invoices, purchase orders, waybills, etc.) havefollow thea samegeneral structureformat, but theirlayout appearancevaries dependsby onvendor several items and otheror parameters. Capturing data fromrequires thesemore documentsadvanced is a complex, but solvable taskmethods.<ref>{{Cite book|last1=Yi|first1=Jeonghee|last2=Sundaresan|first2=Neel|title=Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD '00|date=2000|chapter=A classifier for semi-structured documents|pages=340–344|doi=10.1145/347090.347164|isbn=1581132336|citeseerx=10.1.1.87.2662|s2cid=2154084 }}</ref>
'''Unstructured documents''' (letters, contracts, articles, etc.) could be flexible with structure and appearance.<ref name=":0" />
==The Internet and the future==
Advocates for the growth of AIDC systems argue that AIDC has the potential to greatly increase industrial efficiency and general quality of life. If widely implemented, the technology could reduce or eliminate counterfeiting, theft, and product waste, while improving the efficiency of supply chains.<ref>{{cite book |last = Waldner |first = Jean-Baptiste |author-link = Jean-Baptiste Waldner |title = Nanocomputers and Swarm Intelligence |publisher = [[ISTE Ltd|ISTE]] [[John Wiley & Sons]] |place = London |year = 2008 | pages = 205–214 |isbn = 978-1-84704-002-2}}</ref> However, others have voiced criticisms of the potential expansion of AIDC systems into everyday life, citing concerns over personal privacy, consent, and security.<ref>{{cite web|last=Glaser|first=April|title=Biometrics Are Coming, Along With Serious Security Concerns|url=https://www.wired.com/2016/03/biometrics-coming-along-serious-security-concerns/|website=www.wired.com|access-date=5 July 2021|date=9 March 2016}}</ref>
The global association [[Auto-ID Labs]] wasassociation, founded in 1999, andincludes is made up of 100 of the largest companies in themajor worldcorporations such as [[Wal-Mart|Walmart]], [[Coca-Cola]], [[Gillette (brand)|Gillette]], [[Johnson & Johnson]], [[Pfizer]], [[Procter & Gamble]], [[Unilever]], [[United Parcel Service|UPS]], companiesand workingtech infirms the sector of technology such aslike [[SAP]], Alien, and Sun, asalong well aswith five academic research centers.<ref>{{cite web |url= http://www.ifm.eng.cam.ac.uk/automation/documents/centerguide.pdf |title= The New Network |author= Auto-ID Center |access-date= 23 June 2011 |archive-date= 22 March 2016 |archive-url= https://web.archive.org/web/20160322062919/http://www.ifm.eng.cam.ac.uk/automation/documents/centerguide.pdf/ |url-status= dead }}</ref> These centers are based at the following Universities; [[Massachusetts Institute of Technology]] in the (USA), the [[University of Cambridge]] in the (UK), the [[University of Adelaide]] in (Australia), [[Keio University]] in (Japan),<ref>{{Cite andweb |title=Auto-ID Laborator|url=https://www.kri.sfc.keio.ac.jp/en/lab/autoid/|access-date=2025-06-27|website=Keio Research Institute at SFC }}</ref> [[ETH Zurich]], as well as theand [[University of St. Gallen]] in (Switzerland).
The Auto-ID Labs suggests a concept ofenvisions a future supply chain that is based on the Internet of objects,Objects i.e.,— a global application of RFID. TheyTheir trygoal is to harmonize technology, processes, and organization. Research is focusedfocuses on miniaturization (aiming for a size oftargeting 0.3 mm/ per chip), cost reduction in the price per single device (aiming at around $0.05 per unit), the development ofand innovative applications such as payment without any physicalcontactless contactpayments ([[Sony]]/[[Philips]]), [[domotics]] (clothese.g., equippedtagged with radio tagsclothing and intelligent washing machinesappliances), and sporting events (e.g., timing at the [[Berlin Marathon]]).
==AIDC 100==
[[AIDC 100]] is a professional organization for the automatic identification and data capture (AIDC) industry. This group is composed of individuals who made substantial contributions to the advancement of the industry. Increasing business's understanding of AIDC processes and technologies are the primary goals of the organization.<ref>{{cite web|title=AIDC 100|url=http://www.aidc100.org|work=AIDC 100: Professionals Who Excel in Serving the AIDC Industry|access-date=2 August 2011| archive-url= https://web.archive.orgtoday/web/2011072423093720120720224452/http://www.aidc100.org/| archive-date= 24 July 2011 2012-07-20| url-status= live}}</ref>
==See also==
* [[Field Service Management]]
* [[Mobile Enterprise]]
* [[Mobile asset management|Mobile Asset Management]]
* [[Smart data capture]]
* [[Ubiquitous computing]]
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