Optical Character Recognition (OCR) is a process that can be used to convert scanned images of text into machine-readable text. OCR can be used to convert images of documents, such as PDFs or scanned paper documents, into editable text. OCR technology can also be used to recognize text in images, such as street signs or license plates.
While OCR technology has a wide range of applications, it is most commonly used in the text analytics industry for extracting text from images of documents. This can be useful for converting scanned PDFs or paper documents into digital text files that can be processed and analyzed using text analytics software.
OCR technology can also be used outside of the text analytics industry. For example, OCR can be used to recognize text in images, such as street signs or license plates. This can be useful for applications like automated vehicle navigation.
Types of Text recognition Technologies
When comparing Optical Character Recognition to other terms, it is important to note that there are a few different types of text recognition technologies. Some of these other technologies include:
- Optical mark recognition (OMR): This technology is used to recognize characters or marks that have been made by hand, such as checkboxes or circles.
- Intelligent character recognition (ICR): This technology is used to recognize characters that have been typed or handwritten.
- Handwriting recognition: This technology is used to recognize handwritten text.
Optical Character Recognition is just one type of text recognition technology, and it has a specific use case within the text analytics industry. When considering which type of text recognition technology to use for a specific application, it is important to consider the different types of text that need to be recognized and the accuracy requirements for the application.