OCR technology and its various applications
Optical character recognition or OCR for short is a widespread technology that is used to recognize text inside of a variety of different images such as scanned documents and photos. OCR technology is used to convert images containing written text into machine readable text data. First popularized in the early 1990s for the purposes of digitizing historic newspapers, the technology has since undergone several developments and improvements. Current day OCR solutions can deliver near perfect accuracy, while more advanced methods such as Zonal OCR can be used to automate the processes of complex document workflows.
What is OCR technology used for?
The most common and well known use for OCR technology is for the conversion of printed text and paper documents in machine readable text. After a scanned paper document has gone through OCR processing, it can then be edited with popular online word processing programs such as Microsoft Word, Open Office, and Google Docs. Before the advent of OCR technology, this process had to be done manually by hand. In keeping with the example of historic newspapers, the only way to digitize these old documents prior to OCR technology was to manually retype all of the text, which was obviously a process that was both time consuming and prone to error.
In addition to converting paper documents into machine readable texts, OCR technology can also be used in banks for improved transaction security and risk management, for digitizing loan documents, and for providing two layer security at ATMs that make use of facial recognition technology. What’s more, OCR technology is also used in less known fields such as the automation of data entry, indexing documents for search engines, automatic number plate recognition, and assisting people who are blind or otherwise visually impaired.
What are the benefits of OCR technology?
One of the primary benefits of OCR technology is that it improves information accessibility for users. In this context, another common application of OCR technology is for the automatic conversion of image based PDF, TIFF, or JPG files into text based machine readable files. Examples of such files can include receipts, invoices, contracts, and financial statements. After these files are converted into OCR’s, the following functions can then be performed:
- Search from a large repository to find the correct document.
- Use search capabilities to view each document.
- Make edits to documents when corrections need to be made.
- Repurpose documents with extracted text sent to other systems.
As such, businesses who employ the use of OCR capabilities to convert PDF documents and images can save valuable time, effort, and resources that would have to otherwise be dedicated to managing unsearchable data. Once transferred, textual information that has been processed using OCR technology can then be used by business in an easier and more efficient manner. What’s more, OCR technology can also assist businesses in eliminating manual data entry, reducing errors, and reallocating physical storage space.
In addition to extracting machine printed text from digital images, OCR technology can also be used to extract documents from a variety of other sources and formats including hand printed text (ICR), check boxes (OMR), and barcodes, among a host of others. In turn, this can further be of assistance to business operations by centralizing and securing data, automating document processing and routing, and improving upon customer service by ensuring that employees have the most up to date and accurate information when they need it. Furthermore, this can also help businesses reduce costs even further.
As technology has changed the landscape of the professional workplace, documents and written text have proven to be no exception. While digitizing and otherwise reproducing documents was once a laborious and tedious process, OCR technology has made the process more manageable than ever before. With the advent of OCR technology, businesses can improve upon their functions by automating tasks that have traditionally been manual. As such, the efforts that had previously gone to complete these manual tasks can instead be applied to other business processes and functions.