Automatic identification and data capture: Difference between revisions

Revision as of 17:19, 5 July 2021

Latest revision as of 01:07, 16 July 2025

(18 intermediate revisions by 17 users not shown)

Line 1:

{{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 of 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 voice printvoiceprint which involves audio data, and the rest all involvesinvolve 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 [[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 ==

{{unsourcedunreferenced section|date=July 2021}}

Nearly all of 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 a 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, usuallytypically 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=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}}

*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 it 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 thelayouts, same structure and appearance. It is the easiest type 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 number of 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 2016-03-09}}</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[[Massachusetts Universities;Institute MITof in theTechnology]] (USA), Cambridge [[University inof theCambridge]] (UK), the [[University of Adelaide]] in (Australia), [[Keio University]] in (Japan),<ref>{{Cite andweb ETH|title=Auto-ID ZurichLaborator|url=https://www.kri.sfc.keio.ac.jp/en/lab/autoid/|access-date=2025-06-27|website=Keio asResearch wellInstitute asat theSFC }}</ref> [[ETH Zurich]] and [[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&nbsp; mm/ per chip), cost reduction in the price per single device (aiming at around $0.05 per unit), the development ofand innovative applicationapplications such as payment without any physicalcontactless contactpayments ([[Sony]]/[[Philips]]), [[domotics]] (clothes equipped withe.g., radiotagged tagsclothing and intelligent washing machinesappliances), and sporting events (e.g., timing at the [[Berlin marathonMarathon]]).

==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==

Line 52:

* [[Automatic number-plate recognition]]

* [[Auto-ID Labs]]

* [[Data privacy]]

* [[Device management]]

* [[Digital Mailroom]]

* [[Face recognition]]

* [[Field Service Management]]

* [[Mobile Enterprise]]

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

* [[Smart data capture]]

* [[Ubiquitous computing]]

* [[Ubiquitous Commerce]]

* [[Digital Mailroom]]

* [[Face recognition]]

* [[Data privacy]]

{{colend}}