What is OCR Technology?
Suppose you wanted to digitize a magazine article or a printed contract. You could spend hours retyping and then correcting misprints. Or you could convert all the required materials into digital format in several minutes using a scanner (or a digital camera) and Optical Character Recognition software.
What exactly is meant by OCR?
Optical Character Recognition, or OCR, is a technology that enables you to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data.
Imagine you’ve got a paper document – for example, magazine article, brochure, or PDF contract your partner sent to you by email. Obviously, a scanner is not enough to make this information available for editing, say in Microsoft Word. All a scanner can do is create an image or a snapshot of the document that is nothing more than a collection of black and white or color dots, known as a raster image. In order to extract and repurpose data from scanned documents, camera images or image-only PDFs, you need OCR software that would single out letters on the image, put them into words and then – words into sentences, thus enabling you to access and edit the content of the original document.
What Technology lies behind OCR?
The exact mechanisms that allow humans to recognize objects are yet to be understood, but the three basic principles are already well known by scientists – integrity, purposefulness and adaptability (IPA*). These principles constitute the core of Free OCR to Word allowing it to replicate natural or human-like recognition.
Let’s take a look on how Free OCR to Word recognizes text. First, the program analyzes the structure of document image. It divides the page into elements such as blocks of texts, tables, images, etc. The lines are divided into words and then – into characters. Once the characters have been singled out, the program compares them with a set of pattern images. It advances numerous hypotheses about what this character is. Basing on these hypotheses the program analyzes different variants of breaking of lines into words and words into characters. After processing huge number of such probabilistic hypotheses, the program finally takes the decision, presenting you the recognized text.