Automatic identification and data capture

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Automatic identification and data capture (AIDC) refers to the methods of automatically identifying objects, collecting data about them, and entering them directly into computer systems, without human involvement. Technologies typically considered as part of AIDC include QR codes, [1] bar codes, radio frequency identification (RFID), biometrics (like iris and facial recognition system), magnetic stripes, optical character recognition (OCR), smart cards, and voice recognition. AIDC is also commonly referred to as "Automatic Identification", "Auto-ID" and "Automatic Data Capture". [2]

Contents

AIDC is the process or means of obtaining external data, particularly through the analysis of images, sounds, or videos. 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 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

Nearly all the automatic identification technologies consist of three principal components, which also comprise the sequential steps in AIDC:

  1. 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.
  2. Machine reader or scanner. This device reads the encoded data, converting them to an alternative form, typically an electrical analog signal.
  3. 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 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 ]

These basic technologies allow extracting information from paper documents for further processing 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,and unstructured .[ citation needed ]

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 ]

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. [7]

Unstructured documents (letters, contracts, articles, etc.) could be flexible with structure and appearance.

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. [8] However, others have voiced criticisms of the potential expansion of AIDC systems into everyday life, citing concerns over personal privacy, consent, and security. [9]

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 Walmart, Coca-Cola, Gillette, Johnson & Johnson, Pfizer, Procter & Gamble, Unilever, UPS, companies working in the sector of technology such as SAP, Alien, Sun as well as five academic research centers. [10] 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 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. [11]

See also

Related Research Articles

<span class="mw-page-title-main">Optical character recognition</span> Computer recognition of visual text

Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo or from subtitle text superimposed on an image.

<span class="mw-page-title-main">Barcode</span> Optical machine-readable representation of data

A barcode or bar code is a method of representing data in a visual, machine-readable form. Initially, barcodes represented data by varying the widths, spacings and sizes of parallel lines. These barcodes, now commonly referred to as linear or one-dimensional (1D), can be scanned by special optical scanners, called barcode readers, of which there are several types. Later, two-dimensional (2D) variants were developed, using rectangles, dots, hexagons and other patterns, called matrix codes or 2D barcodes, although they do not use bars as such. 2D barcodes can be read using purpose-built 2D optical scanners, which exist in a few different forms. 2D barcodes can also be read by a digital camera connected to a microcomputer running software that takes a photographic image of the barcode and analyzes the image to deconstruct and decode the 2D barcode. A mobile device with a built-in camera, such as smartphone, can function as the latter type of 2D barcode reader using specialized application software.

Magnetic ink character recognition code, known in short as MICR code, is a character recognition technology used mainly by the banking industry to streamline the processing and clearance of cheques and other documents. MICR encoding, called the MICR line, is at the bottom of cheques and other vouchers and typically includes the document-type indicator, bank code, bank account number, cheque number, cheque amount and a control indicator. The format for the bank code and bank account number is country-specific.

Optical Mark Recognition (OMR), collects data from people by identifying markings on a paper. OMR enables the hourly processing of hundreds or even thousands of documents. For instance, students may remember completing quizzes or surveys that required them to use a pencil to fill in bubbles on paper. A teacher or teacher's aide would fill out the form, then feed the cards into a system that grades or collects data from them.

Enterprise content management (ECM) extends the concept of content management by adding a timeline for each content item and, possibly, enforcing processes for its creation, approval, and distribution. Systems using ECM generally provide a secure repository for managed items, analog or digital. They also include one methods for importing content to bring manage new items, and several presentation methods to make items available for use. Although ECM content may be protected by digital rights management (DRM), it is not required. ECM is distinguished from general content management by its cognizance of the processes and procedures of the enterprise for which it is created.

A multiline optical-character reader, or MLOCR, is a type of mail sorting machine that uses optical character recognition (OCR) technology to determine how to route mail through the postal system.

Intelligent character recognition (ICR) is used to extract handwritten text from image images using ICR, also referred to as intelligent OCR. It is a more sophisticated type of OCR technology that recognizes different handwriting styles and fonts to intelligently interpret data on forms and physical documents.

A card reader is a data input device that reads data from a card-shaped storage medium. The first were punched card readers, which read the paper or cardboard punched cards that were used during the first several decades of the computer industry to store information and programs for computer systems. Modern card readers are electronic devices that can read plastic cards embedded with either a barcode, magnetic strip, computer chip or another storage medium.

Smart Label, also called Smart Tag, is an extremely flat configured transponder under a conventional print-coded label, which includes chip, antenna and bonding wires as a so-called inlay. The labels, made of paper, fabric or plastics, are prepared as a paper roll with the inlays laminated between the rolled carrier and the label media for use in specially-designed printer units.

<span class="mw-page-title-main">OCR-A</span> Typeface designed for early computer OCR

OCR-A is a font issued in 1966 and first implemented in 1968. A special font was needed in the early days of computer optical character recognition, when there was a need for a font that could be recognized not only by the computers of that day, but also by humans. OCR-A uses simple, thick strokes to form recognizable characters. The font is monospaced (fixed-width), with the printer required to place glyphs 0.254 cm apart, and the reader required to accept any spacing between 0.2286 cm and 0.4572 cm.

Forms processing is a process by which one can capture information entered into data fields and convert it into an electronic format. This can be done manually or automatically, but the general process is that hard copy data is filled out by humans and then "captured" from their respective fields and entered into a database or other electronic format.

Access-IS develops and manufactures electronic systems designed to accurately capture and transfer information into electronic systems based on 30 years’ experience in image processing, RFID/NFC technology and barcode reading. Access-IS also manufactures specialist keyboards for banking and PoS applications.

Document Capture Software refers to applications that provide the ability and feature set to automate the process of scanning paper documents or importing electronic documents, often for the purposes of feeding advanced document classification and data collection processes. Most scanning hardware, both scanners and copiers, provides the basic ability to scan to any number of image file formats, including: PDF, TIFF, JPG, BMP, etc. This basic functionality is augmented by document capture software, which can add efficiency and standardization to the process.

<span class="mw-page-title-main">David Allais</span> American expert and inventor (born 1933)

David Allais is an American expert and inventor in the fields of bar coding and automatic identification and data capture. As vice president and later president and chief executive officer of Everett, Washington-based Intermec Inc. (NYSE:IN), he built the company from a small startup into the leading manufacturer of bar code and printing equipment. Prior to Allais' role at Intermec, he served as a manager for IBM. Most recently, Allais founded PathGuide Technologies, a Bothell, Washington-based developer of warehouse management systems for distributors.

Zetes Industries S.A. / N.V. is a Belgian technology company headquartered in Brussels with operations in Europe, Africa and the Middle East, employing more than 1 200 people in 22 countries.

Chipless RFID tags are RFID tags that do not require a microchip in the transponder.

ISO/IEC JTC 1/SC 31 Automatic identification and data capture techniques is a subcommittee of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) Joint Technical Committee (JTC) 1, and was established in 1996. SC 31 develops and facilitates international standards, technical reports, and technical specifications in the field of automatic identification and data capture techniques. The first Plenary established three working groups (WGs): Data Carriers, Data Content, and Conformance. Subsequent Plenaries established other working groups: RFID, RTLS, Mobile Item Identification and Management, Security and File Management, and Applications.

ISO/IEC 20248Automatic Identification and Data Capture Techniques – Data Structures – Digital Signature Meta Structure is an international standard specification under development by ISO/IEC JTC 1/SC 31/WG 2. This development is an extension of SANS 1368, which is the current published specification. ISO/IEC 20248 and SANS 1368 are equivalent standard specifications. SANS 1368 is a South African national standard developed by the South African Bureau of Standards.

Barcode library or Barcode SDK is a software library that can be used to add barcode features to desktop, web, mobile or embedded applications. Barcode library presents sets of subroutines or objects which allow to create barcode images and put them on surfaces or recognize machine-encoded text / data from scanned or captured by camera images with embedded barcodes. The library can support two modes: generation and recognition mode, some libraries support barcode reading and writing in the same way, but some libraries support only one mode.

Smart data capture (SDC), also known as 'intelligent data capture' or 'automated data capture', describes the branch of technology concerned with using computer vision techniques like optical character recognition (OCR), barcode scanning, object recognition and other similar technologies to extract and process information from semi-structured and unstructured data sources. IDC characterize smart data capture as an integrated hardware, software, and connectivity strategy to help organizations enable the capture of data in an efficient, repeatable, scalable, and future-proof way. Data is captured visually from barcodes, text, IDs and other objects - often from many sources simultaneously - before being converted and prepared for digital use, typically by artificial intelligence-powered software. An important feature of SDC is that it focuses not just on capturing data more efficiently but serving up easy-to-access, actionable insights at the instant of data collection to both frontline and desk-based workers, aiding decision-making and making it a two-way process.

References

  1. ↑ Automatic Identification and Data Capture (Barcodes, Magnetic Stripe Cards, Smart Cards, OCR Systems, RFID Products & Biometric Systems) Market - Global Forecast to 2023
  2. ↑ "Automatic Identification and Data Collection (AIDC)". www.mhi.org. Retrieved 2021-04-11.
  3. ↑ "What is Optical Character Recognition (OCR)?". www.ukdataentry.com. 2016-07-22. Retrieved 22 July 2016.
  4. ↑ Palmer, Roger C. (1989, Sept) The Basics of Automatic Identification [Electronic version]. Canadian Datasystems, 21 (9), 30-33
  5. ↑ Rouse, Margaret (2009-10-01). "bar code (or barcode)". TechTarget. Archived from the original on 2017-08-10. Retrieved 2017-03-09.
  6. ↑ Technologies, Recogniform. "Optical recognition and data-capture". www.recogniform.com. Retrieved 2015-01-15.
  7. ↑ Yi, Jeonghee; Sundaresan, Neel (2000). "A classifier for semi-structured documents". Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD '00. pp. 340–344. CiteSeerX   10.1.1.87.2662 . doi:10.1145/347090.347164. ISBN   1581132336. S2CID   2154084.
  8. ↑ Waldner, Jean-Baptiste (2008). Nanocomputers and Swarm Intelligence. London: ISTE John Wiley & Sons. pp. 205–214. ISBN   978-1-84704-002-2.
  9. ↑ Glaser, April (9 March 2016). "Biometrics Are Coming, Along With Serious Security Concerns". www.wired.com. Retrieved 5 July 2021.
  10. ↑ Auto-ID Center. "The New Network". Archived from the original (PDF) on 22 March 2016. Retrieved 23 June 2011.
  11. ↑ "AIDC 100". AIDC 100: Professionals Who Excel in Serving the AIDC Industry. Archived from the original on 24 July 2011. Retrieved 2 August 2011.
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