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bd40bc7c7a Retrieved 2013-06-16. Optical Character Recognition Official Unicode Consortium code chart (PDF) 0 1 2 3 4 5 6 7 8 9 A B C D E F U+244x U+245x Notes 1.^ As of Unicode version 9.0 2.^ Grey areas indicate non-assigned code points . Optical character recognition (optical character reader, OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example from a television broadcast). It is widely used as a form of data entry from printed paper data records, whether passport documents, invoices, bank statements, computerised receipts, business cards, mail, printouts of static-data, or any suitable documentation. Accuracy rates of 80% to 90% on neat, clean hand-printed characters can be achieved by pen computing software, but that accuracy rate still translates to dozens of errors per page, making the technology useful only in very limited applications.. Retrieved 2013-06-16. Web based OCR systems for recognising hand-printed text on the fly have become well known as commercial products in recent years[when?] (see Tablet PC history). See also. book scanning for Project Gutenberg Make electronic images of printed documents searchable, e.g.
There are several techniques for solving the problem of character recognition by means other than improved OCR algorithms. These devices that do not have OCR functionality built-in to the operating system will typically use an OCR API to extract the text from the image file captured and provided by the device. The OCR API returns the extracted text, along with information about the location of the detected text in the original image back to the device app for further processing (such as text-to-speech) or display. Archived from the original on December 25, 2014. Forcing better input. andrewt.net. "Detecting Figures and Part Labels in Patents: Competition-Based Development of Image Processing Algorithms". ^ ^ Riedl, C., Zanibbi, R., Hearst, M. Early versions needed to be trained with images of each character, and worked on one font at a time. "The state of the art in online handwriting recognition". Early optical character recognition may be traced to technologies involving telegraphy and creating reading devices for the blind. In 1914, Emanuel Goldberg developed a machine that read characters and converted them into standard telegraph code. Concurrently, Edmund Fournier d'Albe developed the Optophone, a handheld scanner that when moved across a printed page, produced tones that corresponded to specific letters or characters..