Optical Character Recognition

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  • Topic: Optical character recognition, Computer vision, Image scanner
  • Pages : 8 (2373 words )
  • Download(s) : 223
  • Published : April 6, 2009
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Pooja Mehta


1. Introduction.

2. Working of OCR.

3. Ease of use.

4. Accuracy.

5. OCR limitations.

6. Current state of OCR technology.

7. Apllications.


Optical character recognition, usually abbreviated to OCR, is a type of computer software designed to translate images of handwritten or typewritten into machine-editable text, or to translate pictures of characters into a standard encoding scheme representing them (e.g. ASCII or Unicode). OCR began as a field of research in pattern recognition, artificial intelligence and machine vision.

Optical character recognition (OCR) technology offers blind and visually impaired persons the capacity to scan printed text and then speak it back in synthetic speech or save it to a computer. Little technology exists to interpret graphics such as line art, photographs, and graphs into a medium easily accessible to blind and visually impaired persons.


Text capture is a process to convert analogue text based resources into digitally recognizable text resources. These digital text resources can be represented in many ways such as searchable text in indexes to identify documents or page images, or as full text resources. An essential first stage in any text capture process from analogue to digital will be to create a scanned image of the page side. This will provide the base for all other processes. The next stage may then be to use a technology known as Optical Character Recognition to convert the text content into a machine readable format.

Optical Character Recognition (OCR) is a type of document image analysis where a scanned digital image that contains either machine printed or handwritten script is input into an OCR software engine and translating it into an editable machine readable digital text format (like ASCII text).

OCR works by first pre-processing the digital page image into its smallest component parts with layout analysis to find text blocks, sentence/line blocks, word blocks and character blocks. Other features such as lines, graphics, photographs etc are recognised and discarded. The character blocks are then further broken down into components parts, pattern recognized and compared to the OCR engines large dictionary of characters from various fonts and languages. Once a likely match is made then this is recorded and a set of characters in the word block are recognized until all likely characters have been found for the word block. The word is then compared to the OCR engine’s large dictionary of complete words that exist for that language.

These factors of characters and words recognized are the key to OCR accuracy by combining them the OCR engine can deliver much higher levels of accuracy. Modern OCR engines extend this accuracy through more sophisticated pre-processing of source digital images and better algorithms for fuzzy matching, sounds-like matching and grammatical measurements to more accurately establish word accuracy.

OCRs can either be a self-contained unit (stand-alone) or part of a PC-based system. A stand-alone OCR or the scanner of a computer-based OCR system looks something like a small photocopier. It has a glass screen on which print to be "read" is placed. The scanner camera scans the material and, in a few seconds, the page is read out loud using synthetic voice. OCRs can read almost any book, newspaper or other typewritten materials, but they cannot read handwritten material. When hooked up to a PC, scanned material can be converted to Braille, large print, voice, computer accessible files, or other media. In addition to whether the system is stand-alone or works in conjunction with the user's computer, there are many other features to consider depending on the user's needs, including: accuracy of scanning, and whether the product can handle poor-quality print or image print, such as faxes; level of...
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