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OCR technology converts printed materials into editable text files, saving storage space. It requires hardware and software, and can struggle with handwriting. OCR allows for quick and easy searching of large amounts of data, making it valuable for institutions.
Optical character recognition (OCR) is a process of converting printed materials into text or word processing files that can be easily edited and archived. Technology has made it possible to archive such materials using much less storage space than paper materials. OCR technology has had a huge impact on how information is stored, shared and changed. Before optical character recognition, if someone wanted to turn a book into a word processing file, each page would have to be typed word for word.
OCR technology requires both hardware and software. Also, sophisticated OCR systems require an extra circuit board in the computer itself to complete the process. An optical scanner scans text on a page, then breaks up the characters into a series of dots called bitmaps. The software can read the most common characters and distinguish where lines start and end. This bitmap is then translated into computer text.
While optical character recognition has made tremendous progress in recent years, it doesn’t always work well in recognizing handwriting or characters that look similar to handwriting. There are systems in the banking industry that use OCR technology to try to read amounts on handwritten checks, to accommodate the computer’s ability to read routing and account numbers.
To get an idea of the power of OCR, it can be helpful to take a look at a real-world example. Imagine a police department that has all of its criminal records filed in extensive files. While scanning millions of pages is an expensive and time-consuming undertaking, the benefits are enormous.
Once the OCR system has converted the pages into machine-readable text, a detective, for example, could search the entire history in seconds. Manually finding a particular record might not be too difficult, but imagine a detective trying to look up all crimes committed at a particular intersection between 8:00 and 8:30. This example only scratches the surface of the power of searchable text and is just one reason why many companies and institutions are spending millions of dollars OCRing their legacy data.
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