Automatic identification and data capture
|This article needs additional citations for verification. (June 2011)|
Automatic identification and data capture (AIDC) refers to the methods of automatically identifying objects, collecting data about them, and entering that data directly into computer systems (i.e. without human involvement). Technologies typically considered as part of AIDC include bar codes, Radio Frequency Identification (RFID), biometrics, 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.
AIDC also refers to the methods of recognizing objects, getting information about them and entering that data or feeding it directly into computer systems without any human involvement. Automatic identification and data capture technologies include barcodes, RFID, bokodes, OCR, magnetic stripes, smart cards and biometrics (like iris and facial recognition system).
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 (RFID) 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.
Capturing data from printed documents
|This section contains content that is written like an advertisement. (February 2012)|
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
The documents for data capture can be divided into 3 groups: structured, semi-structured and unstructured.
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.
|Developer||Basic Technologies||Data Capture Application||Data Capture SDK|
|ABBYY|| OCR (195 languages),
ICR (113 languages),
OMR, OBR, BCR
|ABBYY FlexiCapture is an intelligent data and document capture software that delivers automated processing of any type of structured, semi-structured and unstructured documents and forms||ABBYY FlexiCapture Engine is a data and document capture SDK for any type of structured, semi-structured and unstructured documents and forms|
|Accusoft|| OCR (118 languages),
ICR (11 languages),
| ImageGear for .NET is an SDK that delivers fully managed code for WinForms, ASP.NET, and WPF application development. Optional Recognition component enables a comprehensive integrated OCR toolkit.|
FormSuite, available for .NET or ActiveX, is a structured forms processing SDK designed to handle forms processing from scanning to recognition. Barcode recognition and creation can also be added.
|AnyDoc Software|| OCR (4 languages),
ICR, OMR, OBR
|OCR for AnyDoc automates data capture from all business documents, including structured, semi-structured, and unstructured documents by incorporating AnyApp Technology for template-free processing.|
|Captricity has a combination of software and crowdsourced human workers. Captricity's shreddr software in combination with OCR software and crowdsourced human workers, transcribes characters on forms and transfers this data to .csv spreadsheets. Only structured and semi-structured data can be transcribed by Captricity.||Captricity's API allows for transcribed forms to be input into internal databases such as SAP & ORACLE|
|Cvision Technologies|| OCR (60 languages),
ICR (60 languages),
|Cvision's Trapeze is an intelligent software that is able to recognize and capture text from structured, semi-structured, and unstructured documents including forms, invoices, and EOBs||Cvision's Trapeze's SDK captures data from structured, semi-structured, and unstructured documents including forms, invoices, and EOBs|
|Expervision|| OCR (18 languages),
ICR (18 languages),
OMR, OBR, BCR
|Expervision TypeReader Expervision TypeReader can automatically process full text documents. In under the premise of accurately identification, its processing speed can reach above 100 pages each minute.||Expervision OpenRTK Engine is an intelligent capture data and document processing SDK. It has flexible language support function, in theory, it can support additional anyone language and train the engine to adapt various fonts according to customize demand. Customized API definition and development are supported.|
|I.R.I.S. Group|| OCR (120 languages),
ICR (Latin based languages),
OMR, OBR, BCR
| IRISCapture for Invoices – invoice processing solution
IRISCapture Pro for Forms is an intelligent software suite that automatically captures, sorts and identifies all types of documents and forms
|LEADTOOLS|| OCR (118 languages),
ICR (15 languages),
OMR, OBR, BCR
|LEADTOOLS Forms Recognition module is a .NET SDK that harnesses the power of LEAD's image processing technology to intelligently identify form components and features that can be used to recognize structured forms|
|Nuance Communications|| OCR (120 languages),
ICR, OMR, OBR, BCR
|OmniPage Professional 17 makes structured forms made easy from start to finish. You can turn paper forms into electronic forms and then collect the data.||OmniPage Capture SDK for Windows with its advanced Logical Form Recognition (LFR) automates form template creation and structured forms processing.|
|PSIGEN Software|| OCR (99 languages),
ICR, OMR, OBR, BCR (1D and 2D)
|PSI:Capture is a complete capture solution that includes all the functionlity required to automatically process all structured and semi-structured documents, including invoices, forms and general mail. One of its key strengths is its unrivalled dynamic interface to SharePoint.|
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 Center 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, Aliens, 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 University of St. Gallen in Switzerland.
The Auto-ID Center 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.
- Auto-ID Labs
- Device management
- Field Service Management
- Mobile Enterprise
- Mobile Asset Management
- Ubiquitous computing
- Ubiquitous Commerce
- Digital Mailroom
- Technologies, Recogniform. "Optical recognition and data-capture". http://www.recogniform.com/. Retrieved 2015-01-15.
- 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.