|
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== 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 These basic technologies allow extracting information from paper documents for further processing it in the enterprise information systems such as ERP, CRM and others. 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. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Automatic identification and data capture」の詳細全文を読む スポンサード リンク
|