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Optical character recognition converts pictures of written or printed text into encoded text. The input is an image of marks; the output is a sequence of characters that can be searched, copied or processed as text. Merely storing a scanned page as a raster image does not perform this recognition.
A typical system must locate text regions and lines, separate or jointly interpret their marks, and decide which character sequence they represent. Image processing can correct skew or improve contrast; image segmentation can identify regions and candidate boundaries. Recognition can use visual evidence together with constraints from surrounding text. These stages can fail separately: a correct symbol classifier cannot read a line that the layout analysis omitted.[ref]
An Overview of the Tesseract OCR Engine by Ray Smith. §2, printed p. 629, architecture: binary input, connected components and organization into lines and words; §§3–5, line finding and word recognition
For a concrete ambiguity, the final
rn of modern can resemble m in a degraded scan, producing modem. That single visual merge requires two ordinary character edits to repair: substitute the final m with r, then insert n. With six reference characters, the character error rate in this synthetic example isThe edit distance makes the scoring rule explicit; one visual mistake need not count as one character edit.[ref]Quality Assurance in OCR-D by OCR-D. Evaluation Metrics, Levenshtein Distance and Character Error Rate sections
A historical implementation illustrates the engineering choices: the 2007 description of Tesseract starts from a binary image and organizes connected outlines into text lines and words before recognition passes. That is evidence about a particular engine architecture, not a requirement that every recognizer use separate character segmentation. Accuracy depends on the images, writing system, layout and evaluation convention; fluent-looking output can still contain consequential transcription errors.