Allowing for irregularities of printed ink on paper, each algorithm averages the light and darkĪlong the side of a stroke, matches it to known characters and makes a best guess as to which character it is. Today's OCR engines add the multiple algorithms of neural network technology to analyze the stroke edge, the line ofĭiscontinuity between the text characters, and the background. The hit-or-miss results of such pattern-recognition systems helpedĮstablish OCR's reputation for inaccuracy. Older OCR systems match these images against stored bitmaps based on specific fonts. The OCR software then processes these scans to differentiate between images and text and determine what letters are represented in the light and dark areas. Optical scanner (and sometimes a specialized circuit board in the PC) to read in the page as a bitmap (a pattern of dots). Before OCR can be used, the source material must be scanned using an And each year, the technology freesĪcres of storage space once given over to file cabinets and boxes full of paper documents. For many document-input tasks, OCR is the most cost-effective and speedy method available. Advances in OCR technology have spurred its increasing Used by libraries and government agencies to make lengthy documents quickly available electronically. This is an efficient way to turn hard-copy materials into data files that can be edited and otherwise manipulated on a computer. Optical character recognition (OCR) is the translation of optically scanned bitmaps of printed or written text characters into character codes, such as ASCII. Requirements: Matlab, Matlab Image Processing Toolbox, Matlab Neural Network Toolbox.
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January 2023
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