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, 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".
AIDC is the process or means of obtaining external data, particularly through 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 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 print which involves audio data and the rest all involves video data.
Radio-frequency identification is relatively a new AIDC technology which was first developed in 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 of 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 a 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 alternative form, usually 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:
- OCR – for printed text recognition
- ICR – for hand-printed text recognition
- OMR – for marks recognition
- OBR – for barcodes recognition
- BCR – for bar code recognition
- DLR - for document layer recognition
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.
Semi-structured documents (invoices, purchase orders, waybills, etc.) have the same structure but their appearance depends on number of items and other parameters. Capturing data from these documents is a complex, but solvable task.
Unstructured documents (letters, contracts, articles, etc.) could be flexible with structure and appearance.
The Internet and the future
The idea is as simple as its application is difficult. If all cans, books, shoes or parts of cars are equipped with minuscule identifying devices, daily life on our planet will undergo a transformation. Things like running out of stock or wasted products will no longer exist as we will know exactly what is being consumed on the other side of the globe. Theft will be a thing of the past as we will know where a product is at all times. Counterfeiting of critical or costly items such as drugs, repair parts, or electronic components will be reduced or eliminated because manufacturers or other supply chain entities will know where their products are at all times. Product wastage or spoilage will be reduced because environmental sensors will alert suppliers or consumers when sensitive products are exposed to excessive heat, cold, vibration, or other risks. Supply chains will operate far more efficiently because suppliers will ship only the products needed when and where they are needed. Consumer and supplier prices should also drop accordingly.
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, 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. These are based at the following Universities; MIT in the USA, Cambridge University 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 application 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 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.
- Automatic Identification and Data Capture (Barcodes, Magnetic Stripe Cards, Smart Cards, OCR Systems, RFID Products & Biometric Systems) Market - Global Forecast to 2023
- "What is Optical Character Recognition (OCR)?". www.ukdataentry.com. Retrieved 22 July 2016.
- Palmer, Roger C. (1989, Sept) The Basics of Automatic Identification [Electronic version]. Canadian Datasystems, 21 (9), 30-33
- Rouse, Margaret (2009-10-01). "bar code (or barcode)". TechTarget. Retrieved 2017-03-09.
- Technologies, Recogniform. "Optical recognition and data-capture". www.recogniform.com. Retrieved 2015-01-15.
- Yi, Jeonghee (Fall 2000). "A classifier for semi-structured documents": 340–344. doi:10.1145/350000/347164/p340-yi.pdf. Cite journal requires
- Waldner, Jean-Baptiste (2008). Nanocomputers and Swarm Intelligence. London: ISTE John Wiley & Sons. pp. 205–214. ISBN 1-84704-002-0.
- Auto-ID Center. "The New Network" (PDF). Retrieved 23 June 2011.
- "AIDC 100". AIDC 100: Professionals Who Excel in Serving the AIDC Industry. Archived from the original on 24 July 2011. Retrieved 2 August 2011.