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OCR Full Form- Optical Character Recognition

Updated on 23 February, 2024

upGrad Abroad Team

upGrad Abroad Team

upGrad abroad Editorial Team

What is OCR? The full form of OCR is Optical Character Recognition

The method used to transform a text picture into a machine-readable written format is called an OCR. Your computer stores the scan as an image file, for instance, if you scan a document or a receipt. 

The words and characters in the picture file cannot be edited, searched for, or counted using a text editor. Nonetheless, the picture may be transformed into a text document with its contents saved as text data using OCR. 

OCR is a branch of computer vision, artificial intelligence, and pattern recognition research. Software programs with a variety of specializations, including checks, bills, receipts, and legal billing documents, have been created using OCR engines.

How does OCR work?

  • Documents are scanned and converted to binary data using a scanner. After analyzing the scanned picture, the OCR software labels the dark portions as text and the light areas as background.
  • One way to address alignment concerns during the scan process is to deskew or tilt the scanned paper slightly.
  • text image edges being smoothed or any digital picture spots being removed.
  • Organizing the image's boxes and lines.
  • Recognition of scripts for multilingual OCR systems.
  • Pattern identification and feature extraction are the two primary OCR algorithms, or software methods, that OCR software employs for text recognition.
  • The process of pattern matching involves separating a character picture, or glyph, and contrasting it with a glyph that has been stored identically. Only when the input glyph and the saved glyph are of the same font and scale can pattern recognition be used. When scanning photos of papers that were typed in a recognized typeface, this technique performs admirably.
  • The glyphs are broken down or decomposed into characteristics like lines, line direction, closed loops, and line intersections through the process of feature extraction. It then makes use of these attributes to determine which of its numerous stored glyphs is the closest neighbor or the best match.

upGrad Abroad Team

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