Editing Optical character recognition (section)

==Workarounds==
There are several techniques for solving the problem of character recognition by means other than improved OCR algorithms.

===Forcing better input===
Special fonts like [[OCR-A]], [[OCR-B]], or [[MICR]] fonts, with precisely specified sizing, spacing, and distinctive character shapes, allow a higher accuracy rate during transcription in bank check processing. Several prominent OCR engines were designed to capture text in popular fonts such as Arial or Times New Roman, and are incapable of capturing text in these fonts that are specialized and very different from popularly used fonts. As Google Tesseract can be trained to recognize new fonts, it can recognize OCR-A, OCR-B and MICR fonts.<ref>{{Cite web|url=http://trainyourtesseract.com/|title=Train Your Tesseract|date=September 20, 2018|website=Train Your Tesseract|access-date=September 20, 2018}}</ref>

''Comb fields'' are pre-printed boxes that encourage humans to write more legibly{{spaced ndash}}one glyph per box.<ref name="explain" /> These are often printed in a [[Drop out ink|dropout color]] which can be easily removed by the OCR system.<ref name="explain" />

[[Palm OS]] used a special set of glyphs, known as [[Graffiti (Palm OS)|Graffiti]], which are similar to printed English characters but simplified or modified for easier recognition on the platform's computationally limited hardware. Users would need to learn how to write these special glyphs.

Zone-based OCR restricts the image to a specific part of a document. This is often referred to as ''Template OCR''.

===Crowdsourcing===
[[Crowdsourcing]] humans to perform the character recognition can quickly process images like computer-driven OCR, but with higher accuracy for recognizing images than that obtained via computers. Practical systems include the [[Amazon Mechanical Turk]] and [[reCAPTCHA]]. The [[National Library of Finland]] has developed an online interface for users to correct OCRed texts in the standardized ALTO format.<ref>{{cite web|url=http://blogs.helsinki.fi/fennougrica/2014/02/21/ocr-text-editor/|title=What is the point of an online interactive OCR text editor? - Fenno-Ugrica|date=2014-02-21}}</ref> Crowd sourcing has also been used not to perform character recognition directly but to invite software developers to develop image processing algorithms, for example, through the use of [[Tournament theory|rank-order tournaments]].<ref>{{cite journal |author=Riedl, C. |author2=Zanibbi, R. |author3=Hearst, M. A. |author4=Zhu, S. |author5=Menietti, M. |author6=Crusan, J. |author7=Metelsky, I. |author8=Lakhani, K. |title=Detecting Figures and Part Labels in Patents: Competition-Based Development of Image Processing Algorithms |journal=[[International Journal on Document Analysis and Recognition]] |volume=19 |issue=2 |pages=155 |date=20 February 2016 |doi=10.1007/s10032-016-0260-8|arxiv=1410.6751 |s2cid=11873638 }}</ref>