Automatic identification and data capture: Difference between revisions

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{{Short description|Methods of automatically identifying objects by computer system}}

{{Cleanup rewrite|date=July 2021}}

'''Automatic identification and data capture''' ('''AIDC''') refers to the methods of automatically identifying objects, collecting [[Data (computing)|data]] about them, and entering them directly into [[computer]]Computadora systems, without human involvement. Technologies typically considered as part of AIDC include [[QR code]]scodes,<ref>[https://www.apnews.com/61904f62798e4065a041dc9f17759ea4 Automatic Identification and Data Capture (Barcodes, Magnetic Stripe Cards, Smart Cards, OCR Systems, RFID Products & Biometric Systems) Market - Global Forecast to 2023]</ref> [[barBar codes]], [[RFID|radio frequency identification (RFID)]], [[biometrics]]Biometría (like [[iris recognition|iris]] and [[facial recognition system]]), [[magnetic stripe]]sstripes, [[optical character recognition]] (OCR), [[smart cards]], and [[Speech recognition|voice recognition]]. AIDC is also commonly referred to as "Automatic Identification", "Auto-ID" and "Automatic Data Capture".<ref>{{Cite web|title=Automatic Identification and Data Collection (AIDC)|url=https://www.mhi.org/fundamentals/automatic-identification|access-date=2021-04-11|website=www.mhi.org}}</ref>

AIDC is the process or means of obtaining external data, particularly through the [[image analysis|analysis of images]], [[sound]]ssounds, or [[video]]svideos. To capture data, a [[transducer]] is employed which converts the actual image or a sound into a digital file. The file is then stored and at a later time, it can be analyzed by a computer, or compared with other files in a database to verify identity or to provide authorization to enter a secured system. Capturing data can be done in various ways; the best method depends on application.

In biometric security systems, capture is the acquisition of or the process of acquiring and identifying characteristics such as finger image, palm image, facial image, iris print, or voiceprint which involves audio data, and the rest all involve video data.

Radio-frequency identification is relatively a new AIDC technology, which was first developed in the 1980s. The technology acts as a base in automated data collection, identification, and analysis systems worldwide. RFID has found its importance in a wide range of markets, including [[AnimalIdentificación identification|livestockdel identification]]ganado and [[AutomatedIdentificación Vehicleautomatizada Identification]]del vehículo (AVI) systems because of its capability to track moving objects. These automated wireless AIDC systems are effective in manufacturing environments where barcode labels could not survive.

==Overview of automatic identification methods ==

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One of the most useful application tasks of data capture is collecting information from paper documents and saving it into databases (CMS, ECM, and other systems). There are several types of basic technologies used for data capture according to the data type:{{Citation needed|date=April 2013}}

*OCR – for printed text recognition

*[[Optical character recognition|OCR]] – for printed text recognition<ref>{{cite web|title=What is Optical Character Recognition (OCR)?|url=http://www.ukdataentry.com/optical-character-recognition/|website=www.ukdataentry.com|access-date=22 July 2016|date=2016-07-22}}</ref>

*[[Intelligent character recognition|ICR]] – for hand-printed text recognition{{Citation needed|date=April 2013}}

*OMR [[Face– for marks recognition]]

*[[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 – for barcodes recognition

*OBR – for barcodes 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}}</ref>

*BCR – for bar code recognition

*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 allow extracting information from paper documents for further processing in the enterprise information systems such as [[Enterprise resource planning|ERP]], [[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''' (questionnaires, tests, insurance forms, tax returns, ballots, etc.) have completely the same structure and appearance. It is the easiest type for data capture because every data field is located at the same place for all documents.{{Citation needed|date=April 2013}}

'''Semi-structured documents''' (invoices, purchase orders, waybills, etc.) have the same structure, but their appearance depends on several items and other parameters. Capturing data from these documents is a complex, but solvable task.<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.

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==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]] was founded in 1999 and is made up of 100 of the largest companies in the world such as [[Wal-Mart|Walmart]], [[Coca-Cola]], [[Gillette (brand)|Gillette]], [[Johnson & Johnson]], [[Pfizer]], [[Procter & Gamble]], [[Unilever]], [[United Parcel Service|UPS]], companies working in the sector of technology such as [[SAP]], Alien, Sun as well as 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 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, and [[ETH Zurich]], as well as the [[University of St. Gallen]] in Switzerland.

The Auto-ID Labs suggests a concept of a future supply chain that is based on the Internet of objects, i.e., a global application of RFID. They try to harmonize technology, processes, and organization. Research is focused on miniaturization (aiming for a size of 0.3&nbsp;mm/chip), reduction in the price per single device (aiming at around $0.05 per unit), the development of innovative applications such as payment without any physical contact ([[Sony]]/[[Philips]]), [[domotics]] (clothes equipped with radio tags and intelligent washing machines), and sporting events (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.org/web/20110724230937/http://www.aidc100.org/| archive-date= 24 July 2011 | url-status= live}}</ref>

==See also==

{{colbegin}}

* [[Automated species identification]]

* [[Automatic equipment identification]]

* [[Automatic number-plate recognition]]

* [[Auto-ID Labs]]

* [[Device management]]

* [[Field Service Management]]

* [[Mobile Enterprise]]

* [[Mobile asset management|Mobile Asset Management]]

* [[Ubiquitous computing]]

* [[Ubiquitous Commerce]]

* [[Digital Mailroom]]

* [[Face recognition]]

* [[Data privacy]]

{{colend}}

==References==

{{Reflist|30em}}

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