Whereas the first approach involves comparison with a database of characters in different fonts, the latter takes topological features like open areas and line intersections into account. These are organized into lines and then broken down into words by looking at the space in between. Individual characters, finally, are mapped to their digital equivalents using either matrix matching or feature extraction.
#Ocr scanner software software
A popular algorithm to find a suitable threshold is Otsu’s method.Īfter binarization, the OCR software draws outlines around the characters to create so-called connected components. If this threshold is too high, the characters themselves will lose their contours, but if it is too low, artifacts will remain in the image and interfere with the OCR process. These need to be eliminated as much as possible by using an appropriate binarization threshold. However, most scanned images also contain minor artifacts from dust or image compression. The goal is to achieve maximum contrast between the text and the background. How optical character recognition software worksīefore the process of character recognition even begins, the input document must be binarized, or in other words, turned into black and white. In fact, optical character recognition now works so well that it’s easy to forget just how many different steps are necessary to convert analog text into data. Version 4 introduced neural networks for faster and more accurate recognition, making the process even more seamless.
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Tesseract OCR is the most widely used and sits at more than 45,000 stars on GitHub. Initially, all OCR software was proprietary, but nowadays, open-source engines exist as well. Intelligent word recognition (IWR): Whole typewritten or handwritten words, utilizes machine learning.Intelligent character recognition (ICR): Single typewritten or handwritten characters, utilizes machine learning.Optical word recognition (OWR): Whole typewritten words.Optical character recognition (OCR proper): Single typewritten characters.However, there are some important differences between these approaches: OCR is often used as an umbrella term for different kinds of advanced methods of text recognition.
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Application forms, customer records, money orders, receipts, banking statements, insurance certificates, … All of these are now being digitized, making their data readily accessible, reducing the number of printed documents, and minimizing errors caused by manual processing. In commercial use, it also facilitates data entry, machine translation, information mining, and many other processes across industries.īanking and insurance are two sectors that benefit immensely from OCR technology, since they have traditionally relied heavily on paper-based documents. The ability to convert printed paper documents into machine-readable text has made it feasible to digitize entire libraries for future generations. The results can then be processed further on a computer. What began as a means to help the visually impaired has now become a staple of business process automation: OCR, short for “optical character recognition”, is a software technology that recognizes typewritten (or even handwritten) text on scanned document images and converts it into a machine-readable format. But how does OCR software work exactly? And what can we do to achieve better end results? Uses of OCR software Computer-aided text recognition has been making great strides since Hewlett-Packard open-sourced its Tesseract engine and Google took over development in 2006.