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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]] systems, without human involvement. Technologies typically considered as part of AIDC include [[QR code]]s,<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> [[bar codes]], [[RFID|radio frequency identification (RFID)]], [[biometrics]] (like [[iris recognition|iris]] and [[facial recognition system]]), [[magnetic stripe]]s, [[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]]s, or [[video]]s. 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.
==AIDC Companies==▼
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 [[Animal identification|livestock identification]] and [[Automated Vehicle Identification]] (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 ==
{{unreferenced section|date=July 2021}}
Nearly all the automatic identification technologies consist of three principal components, which also comprise the sequential steps in AIDC:
# 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 common applications of data capture is extracting information from paper documents and saving it into databases (CMS, ECM, etc.). Basic technologies used for data capture vary by 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=2025-06-27|website=Amazon Web Services}}</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=2013-04-11|access-date=2025-06-27|website=PDFsoft}}</ref>
* [[Intelligent character recognition|ICR]] – for hand-printed text recognition<ref>{{Cite web|title=ICR - Glossary |url=https://www.digitizationguidelines.gov/term.php?term=icr|access-date=2025-06-27|website=Federal Agencies Digitization Guidelines Initiative}}</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 barcode 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=2025-06-27|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}}
These technologies enable data extraction from paper documents for processing in enterprise systems like [[Enterprise resource planning|ERP]] and [[Customer relationship management|CRM]].{{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, tax returns, insurance forms, ballots) have identical layouts, making data capture straightforward since fields are always in the same ___location.<ref name=":0">{{Cite web|title=Document Understanding - Document types|url=https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/document-types|access-date=2025-06-27|website=docs.uipath.com}}</ref>
'''Semi-structured documents''' (e.g., invoices, purchase orders, waybills) follow a general format, but layout varies by vendor or parameters. Capturing data requires more advanced methods.<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 [[Auto-ID Labs]] association, founded in 1999, includes major corporations such as [[Wal-Mart|Walmart]], [[Coca-Cola]], [[Gillette (brand)|Gillette]], [[Johnson & Johnson]], [[Pfizer]], [[Procter & Gamble]], [[Unilever]], [[United Parcel Service|UPS]], and tech firms like [[SAP]], Alien, and Sun, along with five academic research centers.<ref>{{cite web|url=http://www.ifm.eng.cam.ac.uk/automation/documents/centerguide.pdf|title=The New Network|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 [[Massachusetts Institute of Technology]] (USA), [[University of Cambridge]] (UK), [[University of Adelaide]] (Australia), [[Keio University]] (Japan),<ref>{{Cite web |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]] and [[University of St. Gallen]] (Switzerland).
Auto-ID Labs envisions a future supply chain based on the Internet of Objects — a global application of RFID. Their goal is to harmonize technology, processes, and organization. Research focuses on miniaturization (targeting 0.3 mm per chip), cost reduction (around $0.05 per unit), and innovative applications such as contactless payments ([[Sony]]/[[Philips]]), [[domotics]] (e.g., tagged clothing and intelligent appliances), and sporting events (e.g., timing at the [[Berlin Marathon]]).
[[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://archive.today/20120720224452/http://www.aidc100.org/|archive-date=2012-07-20|url-status=live}}</ref>
==See also==
{{colbegin}}
*[[Auto-ID Labs]]▼
* [[Automated species identification]]
* [[Automatic equipment identification]]
* [[Automatic number-plate recognition]]
▲* [[Auto-ID Labs]]
* [[Data privacy]]
* [[Device management]]
* [[Digital Mailroom]]
* [[Face recognition]]
* [[Field Service Management]]
* [[Mobile Enterprise]]
* [[Mobile asset management]]
* [[Smart data capture]]
* [[Ubiquitous computing]]
* [[Ubiquitous Commerce]]
{{colend}}
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{{Reflist|30em}}
{{DEFAULTSORT:Automatic Identification And Data Capture}}
[[Category:Automatic identification and data capture| ]]
[[Category:Encodings]]
[[Category:Multimodal interaction]]
[[Category:Human–computer interaction]]
[[Category:Radio-frequency identification]]
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