Optical Mark Recognition Software

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Video of the process of scanning and real-time optical character recognition (OCR) with a portable scanner.

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Optical character recognition or 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).^[1]

Simple Checkbox Recognition. Some forms require scanning software to recognize the presence or absence of a mark in a particular location, such as a checkbox, without worrying about the specific shape or symbol drawn therein. The ability to do this is called Optical Mark Recognition, or OMR. Download Optical Mark Recognition Software Advertisement BMP to Word OCR Converter v.2.0 VeryPDF BMP to Word OCR Converter is a Command Line application uses Optical Character Recognition technology to OCR BMP documents to editable Word files, BMP to Word OCR Converter neednt Adobe Acrobat software. Formscanner is a free, open-source OMR (optical mark recognition) software for scanning and grading user-filled, multiple choice forms. Formscanner is easy to use, effective, and completely free of cost and advertisements. Optical mark recognition Software - Free Download optical mark recognition - Top 4 Download - Top4Download.com offers free software downloads for Windows, Mac, iOS and Android computers and mobile devices. Visit for free, full and secured software’s. Optical Mark Recognition software automates the omr form design with the data capture process. The software does this by having it’s own built in form designer for creating and printing omr sheets which are compatible with the software, so they will automatically integrate with the software’s optical mark recognition form processor.

Widely used as a form of information entry from printed paper data records – whether passport documents, invoices, bank statements, computerized receipts, business cards, mail, printouts of static-data, or any suitable documentation – it is a common method of digitizing printed texts so that they can be electronically edited, searched, stored more compactly, displayed on-line, and used in machine processes such as cognitive computing, machine translation, (extracted) text-to-speech, key data and text mining. OCR is a field of research in pattern recognition, artificial intelligence and computer vision.

Early versions needed to be trained with images of each character, and worked on one font at a time. Advanced systems capable of producing a high degree of recognition accuracy for most fonts are now common, and with support for a variety of digital image file format inputs.^[2] Some systems are capable of reproducing formatted output that closely approximates the original page including images, columns, and other non-textual components.

1History
4Techniques
5Workarounds

History[edit]

Early optical character recognition may be traced to technologies involving telegraphy and creating reading devices for the blind.^[3] In 1914, Emanuel Goldberg developed a machine that read characters and converted them into standard telegraph code.^[4] 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.^[5]

In the late 1920s and into the 1930s Emanuel Goldberg developed what he called a 'Statistical Machine' for searching microfilm archives using an optical code recognition system. In 1931 he was granted USA Patent number 1,838,389 for the invention. The patent was acquired by IBM.

Blind and visually impaired users[edit]

In 1974, Ray Kurzweil started the company Kurzweil Computer Products, Inc. and continued development of omni-font OCR, which could recognize text printed in virtually any font (Kurzweil is often credited with inventing omni-font OCR, but it was in use by companies, including CompuScan, in the late 1960s and 1970s^[3]^[6]). Kurzweil decided that the best application of this technology would be to create a reading machine for the blind, which would allow blind people to have a computer read text to them out loud. This device required the invention of two enabling technologies – the CCDflatbed scanner and the text-to-speech synthesizer. On January 13, 1976, the successful finished product was unveiled during a widely reported news conference headed by Kurzweil and the leaders of the National Federation of the Blind.^{[citation needed]} In 1978, Kurzweil Computer Products began selling a commercial version of the optical character recognition computer program. LexisNexis was one of the first customers, and bought the program to upload legal paper and news documents onto its nascent online databases. Two years later, Kurzweil sold his company to Xerox, which had an interest in further commercializing paper-to-computer text conversion. Xerox eventually spun it off as Scansoft, which merged with Nuance Communications.

In the 2000s, OCR was made available online as a service (WebOCR), in a cloud computing environment, and in mobile applications like real-time translation of foreign-language signs on a smartphone. With the advent of smart-phones and smartglasses, OCR can be used in internet connected mobile device applications that extract text captured using the device's camera. These devices that do not have OCR functionality built into the operating system will typically use an OCR API to extract the text from the image file captured and provided by the device.^[7]^[8] 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.

Various commercial and open source OCR systems are available for most common writing systems, including Latin, Cyrillic, Arabic, Hebrew, Indic, Bengali (Bangla), Devanagari, Tamil, Chinese, Japanese, and Korean characters.

Applications[edit]

OCR engines have been developed into many kinds of domain-specific OCR applications, such as receipt OCR, invoice OCR, check OCR, legal billing document OCR.

They can be used for:

Data entry for business documents, e.g. check, passport, invoice, bank statement and receipt
In airports, for passport recognition and information extraction
Automatic insurance documents key information extraction
Extracting business card information into a contact list^[9]
More quickly make textual versions of printed documents, e.g. book scanning for Project Gutenberg
Make electronic images of printed documents searchable, e.g. Google Books
Converting handwriting in real time to control a computer (pen computing)
Defeating CAPTCHA anti-bot systems, though these are specifically designed to prevent OCR.^[10]^[11]^[12] The purpose can also be to test the robustness of CAPTCHA anti-bot systems.
Assistive technology for blind and visually impaired users

Types[edit]

Optical character recognition (OCR) – targets typewritten text, one glyph or character at a time.
Optical word recognition – targets typewritten text, one word at a time (for languages that use a space as a word divider). (Usually just called 'OCR'.)
Intelligent character recognition (ICR) – also targets handwritten printscript or cursive text one glyph or character at a time, usually involving machine learning.
Intelligent word recognition (IWR) – also targets handwritten printscript or cursive text, one word at a time. This is especially useful for languages where glyphs are not separated in cursive script.

OCR is generally an 'offline' process, which analyses a static document. Handwriting movement analysis can be used as input to handwriting recognition.^[13] Instead of merely using the shapes of glyphs and words, this technique is able to capture motions, such as the order in which segments are drawn, the direction, and the pattern of putting the pen down and lifting it. This additional information can make the end-to-end process more accurate. This technology is also known as 'on-line character recognition', 'dynamic character recognition', 'real-time character recognition', and 'intelligent character recognition'.

Techniques[edit]

Pre-processing[edit]

OCR software often 'pre-processes' images to improve the chances of successful recognition. Techniques include:^[14]

De-skew – If the document was not aligned properly when scanned, it may need to be tilted a few degrees clockwise or counterclockwise in order to make lines of text perfectly horizontal or vertical.
Despeckle – remove positive and negative spots, smoothing edges
Binarisation – Convert an image from color or greyscale to black-and-white (called a 'binary image' because there are two colors). The task of binarisation is performed as a simple way of separating the text (or any other desired image component) from the background.^[15] The task of binarisation itself is necessary since most commercial recognition algorithms work only on binary images since it proves to be simpler to do so.^[16] In addition, the effectiveness of the binarisation step influences to a significant extent the quality of the character recognition stage and the careful decisions are made in the choice of the binarisation employed for a given input image type; since the quality of the binarisation method employed to obtain the binary result depends on the type of the input image (scanned document, scene text image, historical degraded document etc.).^[17]^[18]
Line removal – Cleans up non-glyph boxes and lines
Layout analysis or 'zoning' – Identifies columns, paragraphs, captions, etc. as distinct blocks. Especially important in multi-column layouts and tables.
Line and word detection – Establishes baseline for word and character shapes, separates words if necessary.
Script recognition – In multilingual documents, the script may change at the level of the words and hence, identification of the script is necessary, before the right OCR can be invoked to handle the specific script.^[19]
Character isolation or 'segmentation' – For per-character OCR, multiple characters that are connected due to image artifacts must be separated; single characters that are broken into multiple pieces due to artifacts must be connected.
Normalize aspect ratio and scale^[20]

Segmentation of fixed-pitch fonts is accomplished relatively simply by aligning the image to a uniform grid based on where vertical grid lines will least often intersect black areas. For proportional fonts, more sophisticated techniques are needed because whitespace between letters can sometimes be greater than that between words, and vertical lines can intersect more than one character.^[21]

Character recognition[edit]

There are two basic types of core OCR algorithm, which may produce a ranked list of candidate characters.^[22]

Matrix matching involves comparing an image to a stored glyph on a pixel-by-pixel basis; it is also known as 'pattern matching', 'pattern recognition', or 'image correlation'. This relies on the input glyph being correctly isolated from the rest of the image, and on the stored glyph being in a similar font and at the same scale. This technique works best with typewritten text and does not work well when new fonts are encountered. This is the technique the early physical photocell-based OCR implemented, rather directly.

Feature extraction decomposes glyphs into 'features' like lines, closed loops, line direction, and line intersections. The extraction features reduces the dimensionality of the representation and makes the recognition process computationally efficient. These features are compared with an abstract vector-like representation of a character, which might reduce to one or more glyph prototypes. General techniques of feature detection in computer vision are applicable to this type of OCR, which is commonly seen in 'intelligent' handwriting recognition and indeed most modern OCR software.^[23]Nearest neighbour classifiers such as the k-nearest neighbors algorithm are used to compare image features with stored glyph features and choose the nearest match.^[24]

Software such as Cuneiform and Tesseract use a two-pass approach to character recognition. The second pass is known as 'adaptive recognition' and uses the letter shapes recognized with high confidence on the first pass to recognize better the remaining letters on the second pass. This is advantageous for unusual fonts or low-quality scans where the font is distorted (e.g. blurred or faded).^[21]

The OCR result can be stored in the standardized ALTO format, a dedicated XML schema maintained by the United States Library of Congress.

For a list of optical character recognition software see Comparison of optical character recognition software.

Post-processing[edit]

OCR accuracy can be increased if the output is constrained by a lexicon – a list of words that are allowed to occur in a document.^[14] This might be, for example, all the words in the English language, or a more technical lexicon for a specific field. This technique can be problematic if the document contains words not in the lexicon, like proper nouns. Tesseract uses its dictionary to influence the character segmentation step, for improved accuracy.^[21]

The output stream may be a plain text stream or file of characters, but more sophisticated OCR systems can preserve the original layout of the page and produce, for example, an annotated PDF that includes both the original image of the page and a searchable textual representation.

'Near-neighbor analysis' can make use of co-occurrence frequencies to correct errors, by noting that certain words are often seen together.^[25] For example, 'Washington, D.C.' is generally far more common in English than 'Washington DOC'.

Knowledge of the grammar of the language being scanned can also help determine if a word is likely to be a verb or a noun, for example, allowing greater accuracy.

The Levenshtein Distance algorithm has also been used in OCR post-processing to further optimize results from an OCR API.^[26]

Application-specific optimizations[edit]

In recent years,^[when?] the major OCR technology providers began to tweak OCR systems to deal more efficiently with specific types of input. Beyond an application-specific lexicon, better performance may be had by taking into account business rules, standard expression,^{[clarification needed]} or rich information contained in color images. This strategy is called 'Application-Oriented OCR' or 'Customized OCR', and has been applied to OCR of license plates, invoices, screenshots, ID cards, driver licenses, and automobile manufacturing.

The New York Times has adapted the OCR technology into a proprietary tool they entitle, Document Helper, that enables their interactive news team to accelerate the processing of documents that need to be reviewed. They note that it enables them to process what amounts to as many as 5,400 pages per hour in preparation for reporters to review the contents.^[27]

Workarounds[edit]

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

Forcing better input[edit]

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. Ironically however, 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 much 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^[28].

'Comb fields' are pre-printed boxes that encourage humans to write more legibly – one glyph per box.^[25] These are often printed in a 'dropout color' which can be easily removed by the OCR system.^[25]

Palm OS used a special set of glyphs, known as '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[edit]

Crowdsourcing humans to perform the character recognition can quickly process images like computer-driven OCR, but with higher accuracy for recognizing images than is obtained with 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.^[29] 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 rank-order tournaments.^[30]

Accuracy[edit]

Commissioned by the U.S. Department of Energy (DOE), the Information Science Research Institute (ISRI) had the mission to foster the improvement of automated technologies for understanding machine printed documents, and it conducted the most authoritative of the Annual Test of OCR Accuracy from 1992 to 1996.^[31]

Recognition of Latin-script, typewritten text is still not 100% accurate even where clear imaging is available. One study based on recognition of 19th- and early 20th-century newspaper pages concluded that character-by-character OCR accuracy for commercial OCR software varied from 81% to 99%;^[32] total accuracy can be achieved by human review or Data Dictionary Authentication. Other areas—including recognition of hand printing, cursive handwriting, and printed text in other scripts (especially those East Asian language characters which have many strokes for a single character)—are still the subject of active research. The MNIST database is commonly used for testing systems' ability to recognise handwritten digits.

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Accuracy rates can be measured in several ways, and how they are measured can greatly affect the reported accuracy rate. For example, if word context (basically a lexicon of words) is not used to correct software finding non-existent words, a character error rate of 1% (99% accuracy) may result in an error rate of 5% (95% accuracy) or worse if the measurement is based on whether each whole word was recognized with no incorrect letters.^[33]

An example of the difficulties inherent in digitizing old text is the inability of OCR to differentiate between the 'long s' and 'f' characters.^[34]

Web-based OCR systems for recognizing hand-printed text on the fly have become well known as commercial products in recent years^[when?] (see Tablet PC history). 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.^{[citation needed]}

Recognition of cursive text is an active area of research, with recognition rates even lower than that of hand-printed text. Higher rates of recognition of general cursive script will likely not be possible without the use of contextual or grammatical information. For example, recognizing entire words from a dictionary is easier than trying to parse individual characters from script. Reading the Amount line of a cheque (which is always a written-out number) is an example where using a smaller dictionary can increase recognition rates greatly. The shapes of individual cursive characters themselves simply do not contain enough information to accurately (greater than 98%) recognize all handwritten cursive script.^{[citation needed]}

Most programs allow users to set 'confidence rates'. This means that if the software does not achieve their desired level of accuracy, a user can be notified for manual review.

Unicode[edit]

Characters to support OCR were added to the Unicode Standard in June 1993, with the release of version 1.1.

Some of these characters are mapped from fonts specific to MICR, OCR-A or OCR-B.

Optical Character Recognition^[1]^[2]
Official Unicode Consortium code chart (PDF)

U+244x

⑀

⑁

⑂

⑃

⑄

⑅

⑆

⑇

⑈

⑉

⑊

U+245x

Notes

1.^ As of Unicode version 12.0

2.^ Grey areas indicate non-assigned code points

References[edit]

^OnDemand, HPE Haven. 'OCR Document'.
^OnDemand, HPE Haven. 'undefined'.
^ ^a^bSchantz, Herbert F. (1982). The history of OCR, optical character recognition. [Manchester Center, Vt.]: Recognition Technologies Users Association. ISBN9780943072012.
^Dhavale, Sunita Vikrant. Advanced Image-Based Spam Detection and Filtering Techniques. Hershey, PA: IGI Global. p. 91. ISBN9781683180142. Retrieved September 27, 2019.
^d'Albe, E. E. F. (July 1, 1914). 'On a Type-Reading Optophone'. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 90 (619): 373–375. Bibcode:1914RSPSA.90.373D. doi:10.1098/rspa.1914.0061.
^'The History of OCR'. Data Processing Magazine. 12: 46. 1970.
^'Extracting text from images using OCR on Android'. June 27, 2015.
^'[Tutorial] OCR on Google Glass'. October 23, 2014.
^'[javascript] Using OCR and Entity Extraction for LinkedIn Company Lookup'. July 22, 2014.
^'How To Crack Captchas'. andrewt.net. June 28, 2006. Retrieved June 16, 2013.
^'Breaking a Visual CAPTCHA'. Cs.sfu.ca. December 10, 2002. Retrieved June 16, 2013.
^John Resig (January 23, 2009). 'John Resig – OCR and Neural Nets in JavaScript'. Ejohn.org. Retrieved June 16, 2013.
^Tappert, C. C.; Suen, C. Y.; Wakahara, T. (1990). 'The state of the art in online handwriting recognition'. IEEE Transactions on Pattern Analysis and Machine Intelligence. 12 (8): 787. doi:10.1109/34.57669.
^ ^a^b'Optical Character Recognition (OCR) – How it works'. Nicomsoft.com. Retrieved June 16, 2013.
^Sezgin, Mehmet; Sankur, Bulent (2004). 'Survey over image thresholding techniques and quantitative performance evaluation'(PDF). Journal of Electronic Imaging. 13 (1): 146. Bibcode:2004JEI..13.146S. doi:10.1117/1.1631315. Retrieved May 2, 2015.
^Gupta, Maya R.; Jacobson, Nathaniel P.; Garcia, Eric K. (2007). 'OCR binarisation and image pre-processing for searching historical documents'(PDF). Pattern Recognition. 40 (2): 389. doi:10.1016/j.patcog.2006.04.043. Retrieved May 2, 2015.
^Trier, Oeivind Due; Jain, Anil K. (1995). 'Goal-directed evaluation of binarisation methods'(PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 17 (12): 1191–1201. doi:10.1109/34.476511. Retrieved May 2, 2015.
^Milyaev, Sergey; Barinova, Olga; Novikova, Tatiana; Kohli, Pushmeet; Lempitsky, Victor (2013). 'Image binarisation for end-to-end text understanding in natural images'(PDF). Document Analysis and Recognition (ICDAR) 2013. 12th International Conference on. Retrieved May 2, 2015.
^Pati, P.B.; Ramakrishnan, A.G. (May 29, 1987). 'Word Level Multi-script Identification'. Pattern Recognition Letters. 29 (9): 1218–1229. doi:10.1016/j.patrec.2008.01.027.
^'Basic OCR in OpenCV Damiles'. Blog.damiles.com. November 20, 2008. Retrieved June 16, 2013.
^ ^a^b^cRay Smith (2007). 'An Overview of the Tesseract OCR Engine'(PDF). Retrieved May 23, 2013.
^'OCR Introduction'. Dataid.com. Retrieved June 16, 2013.
^'How OCR Software Works'. OCRWizard. Retrieved June 16, 2013.
^'The basic pattern recognition and classification with openCV Damiles'. Blog.damiles.com. November 14, 2008. Retrieved June 16, 2013.
^ ^a^b^c'How does OCR document scanning work?'. Explain that Stuff. January 30, 2012. Retrieved June 16, 2013.
^'How to optimize results from the OCR API when extracting text from an image? - Haven OnDemand Developer Community'.
^Fehr, Tiff, How We Sped Through 900 Pages of Cohen Documents in Under 10 Minutes, Times Insider, The New York Times, March 26, 2019
^'Train Your Tesseract'. Train Your Tesseract. September 20, 2018. Retrieved September 20, 2018.
^'What is the point of an online interactive OCR text editor? - Fenno-Ugrica'. February 21, 2014.
^Riedl, C.; Zanibbi, R.; Hearst, M. A.; Zhu, S.; Menietti, M.; Crusan, J.; Metelsky, I.; Lakhani, K. (February 20, 2016). 'Detecting Figures and Part Labels in Patents: Competition-Based Development of Image Processing Algorithms'. International Journal on Document Analysis and Recognition. 19 (2): 155. arXiv:1410.6751. doi:10.1007/s10032-016-0260-8.
^'Code and Data to evaluate OCR accuracy, originally from UNLV/ISRI'. Google Code Archive.
^Holley, Rose (April 2009). 'How Good Can It Get? Analysing and Improving OCR Accuracy in Large Scale Historic Newspaper Digitisation Programs'. D-Lib Magazine. Retrieved January 5, 2014.
^Suen, C.Y.; Plamondon, R.; Tappert, A.; Thomassen, A.; Ward, J.R.; Yamamoto, K. (May 29, 1987). Future Challenges in Handwriting and Computer Applications. 3rd International Symposium on Handwriting and Computer Applications, Montreal, May 29, 1987. Retrieved October 3, 2008.
^Sarantos Kapidakis, Cezary Mazurek, Marcin Werla (2015). Research and Advanced Technology for Digital Libraries. Springer. p. 257. ISBN9783319245928. Retrieved April 3, 2018.CS1 maint: multiple names: authors list (link)

External links[edit]

Wikimedia Commons has media related to Optical character recognition.

Unicode OCR – Hex Range: 2440-245F Optical Character Recognition in Unicode

Retrieved from 'https://en.wikipedia.org/w/index.php?title=Optical_character_recognition&oldid=918199598'

(Redirected from Optical Mark Recognition)

Optical mark recognition (also called optical mark reading and OMR) is the process of capturing human-marked data from document forms such as surveys and tests. They are used to read questionnaires, multiple choice examination paper in the form of lines or shaded areas.

1OMR background
4Usage

OMR background[edit]

OMR test form, with registration marks and drop-out colors, designed to be scanned by dedicated OMR device

Many traditional OMR devices work with a dedicated scanner device that shines a beam of light onto the form paper. The contrasting reflectivity at predetermined positions on a page is then used to detect these marked areas because they reflect less light than the blank areas of the paper.

Some OMR devices use forms that are preprinted onto 'transoptic' paper and measure the amount of light which passes through the paper; thus a mark on either side of the paper will reduce the amount of light passing through the paper.

In contrast to the dedicated OMR device, desktop OMR software allows a user to create their own forms in a word processor and print them on a laser printer. The OMR software then works with a common desktop image scanner with a document feeder to process the forms once filled out.

OMR is generally distinguished from optical character recognition (OCR) by the fact that a complicated pattern recognition engine is not required. That is, the marks are constructed in such a way that there is little chance of not reading the marks correctly. This does require the image to have high contrast and an easily recognizable or irrelevant shape. A related field to OMR and OCR is the recognition of barcodes, such as the UPC bar code found on product packaging.

One of the most familiar applications of optical mark recognition is the use of #2 pencil (HB in Europe) bubble optical answer sheets in multiple choice questionexaminations. Students mark their answers, or other personal information, by darkening circles marked on a pre-printed sheet. Afterwards the sheet is automatically graded by a scanning machine. In the United States and most European countries, a horizontal or vertical 'tick' in a rectangular 'lozenge' is the most commonly used type of OMR form; the most familiar application in the United Kingdom is the UK National lottery form.^{[citation needed]} Lozenge marks are a later technology and have the advantage of being easier to mark and easier to erase. The large 'bubble' marks are legacy technology from very early OMR machines that were so insensitive a large mark was required for reliability. In most Asian countries, a special marker is used to fill in an optical answer sheet. Students, likewise, mark answers or other information by darkening circles marked on a pre-printed sheet. Then the sheet is automatically graded by a scanning machine.

Many of today's OMR applications involve people filling in specialized forms. These forms are optimized for computer scanning, with careful registration in the printing, and careful design so that ambiguity is reduced to the minimum possible. Due to its extremely low error rate, low cost and ease-of-use, OMR is a popular method of tallying votes.^[1]^[2]^[3]^[4]^[5]^[6]^[7]^[8]^[9]^[10]

OMR marks are also added to items of physical mail so folder inserter equipment can be used. The marks are added to each (normally facing/odd) page of a mail document and consist of a sequence of black dashes that folder inserter equipment scans in order to determine when the mail should be folded then inserted in an envelope.

Optical answer sheet[edit]

A response to an SAT math question marked on an optical answer sheet

An optical answer sheet or bubble sheet is a special type of form used in multiple choice questionexaminations. Optical mark recognition is used to detect answers. The most well known company in the United States involved with optical answer sheets is the Scantron Corporation, although certain uses require their own customized system.^{[citation needed]}

Optical answer sheets usually have a set of blank ovals or boxes that correspond to each question, often on separate sheets of paper. Bar codes may mark the sheet for automatic processing, and each series of ovals filled will return a certain value when read. In this way students' answers can be digitally recorded, or identity given.

Reading[edit]

The first optical answer sheets were read by shining a light through the sheet and measuring how much of the light was blocked using phototubes on the opposite side.^[11] As some phototubes are mostly sensitive to the blue end of the visible spectrum,^[12] blue pens could not be used, as blue inks reflect and transmit blue light. Because of this, number two pencils had to be used to fill in the bubbles—graphite is a very opaque substance which absorbs or reflects most of the light which hits it.^[11]

Modern optical answer sheets are read based on reflected light, measuring lightness and darkness. They do not need to be filled in with a number two pencil, though these are recommended over other types due to the lighter marks made by higher-number pencils, and the smudges from number 1 pencils. Black ink will be read, though many systems will ignore marks that are the same color the form is printed in.^[11] This also allows optical answer sheets to be double-sided, because marks made on the opposite side will not interfere with reflectance readings as much as with opacity readings.

Most systems accommodate for human error in filling in ovals imprecisely, as long as they do not stray into the other ovals and the oval is almost completely filled

Errors[edit]

It is possible for optical answer sheets to be printed incorrectly, such that all ovals will be read as filled. This occurs if the outline of the ovals is too thick, or is irregular. During the 2008 U.S. presidential election, this occurred with over 19,000 absentee ballots in the Georgia county of Gwinnett, and was discovered after around 10,000 had already been returned. The slight difference was not apparent to the naked eye, and was not detected until a test run was made in late October. This required all ballots to be transferred to correctly printed ones, by sequestered workers of the board of elections, under close observation by members of the Democratic and Republican (but not other) political parties, and county sheriffdeputies. The transfer, by law, could not occur until election day (November 4).^{[citation needed]}

OMR software[edit]

Plain paper OMR survey form, without registration marks and drop-out colors, designed to be scanned by an image scanner and OMR software

OMR software is a computer software application that makes OMR possible on a desktop computer by using an Image scanner to process surveys, tests, attendance sheets, checklists, and other plain-paper forms printed on a laser printer.

OMR software is used to capture data from OMR sheets. While data capturing scanning devices focus on many factors like thickness of paper dimensions of OMR sheet and designing pattern.

Commercial OMR software

One of the first OMR software packages that used images from common image scanners was Remark Office OMR, made by Gravic, Inc. (originally named Principia Products, Inc.). Remark Office OMR 1.0 was released in 1991.

The need for OMR software originated because early optical mark recognition systems used dedicated scanners and special pre-printed forms with drop-out colors and registration marks. Such forms typically cost US$0.10 to $0.19 a page.^[13] In contrast, OMR software users design their own mark-sense forms with a word processor or built-in form editor, print them locally on a printer, and can save thousands of dollars on large numbers of forms.^[14]

Identifying optical marks within a form, such as for processing census forms, has been offered by many forms-processing (Batch Transaction Capture) companies since the late 1980s. Mostly this is based on a bitonal image and pixel count with minimum and maximum pixel counts to eliminate extraneous marks, such as those erased with a dirty eraser that when converted into a black-and-white image (bitonal) can look like a legitimate mark. So this method can cause problems when a user changes his mind, and so some products started to use grayscale to better identify the intent of the marker—internally scantron and NCS scanners used grayscale.

OMR software is also used for adding OMR marks to mail documents so they can be scanned by folder inserter equipment. An example of OMR software is Mail Markup from UK developer Funasset Limited. This software allows the user to configure and select an OMR sequence then apply the OMR marks to mail documents prior to printing.

History[edit]

Optical mark recognition (OMR) is the scanning of paper to detect the presence or absence of a mark in a predetermined position.^[4] Optical mark recognition has evolved from several other technologies. In the early 19th century and 20th century patents were given for machines that would aid the blind.^[2]

OMR is now used as an input device for data entry. Two early forms of OMR are paper tape and punch cards which use actual holes punched into the medium instead of pencil filled circles on the medium. Paper tape was used as early as 1857 as an input device for telegraph.^[10] Punch cards were created in 1890 and were used as input devices for computers. The use of punch cards declined greatly in the early 1970s with the introduction of personal computers.^[8] With modern OMR, where the presence of a pencil filled in bubble is recognized, the recognition is done via an optical scanner.

The first mark sense scanner was the IBM 805 Test Scoring Machine; this read marks by sensing the electrical conductivity of graphite pencil lead using pairs of wire brushes that scanned the page. In the 1930s, Richard Warren at IBM experimented with optical mark sense systems for test scoring, as documented in US Patents 2,150,256 (filed in 1932, granted in 1939) and 2,010,653 (filed in 1933, granted in 1935). The first successful optical mark-sense scanner was developed by Everett Franklin Lindquist as documented in US Patent 3,050,248 (filed in 1955, granted in 1962). Lindquist had developed numerous standardized educational tests, and needed a better test scoring machine than the then-standard IBM 805. The rights to Lindquist's patents were held by the Measurement Research Center until 1968, when the University of Iowa sold the operation to Westinghouse Corporation.

During the same period, IBM also developed a successful optical mark-sense test-scoring machine, as documented in US Patent 2,944,734 (filed in 1957, granted in 1960). IBM commercialized this as the IBM 1230 Optical mark scoring reader in 1962. This and a variety of related machines allowed IBM to migrate a wide variety of applications developed for its mark sense machines to the new optical technology. These applications included a variety of inventory management and trouble reporting forms, most of which had the dimensions of a standard punched card.

While the other players in the educational testing arena focused on selling scanning services, Scantron Corporation, founded in 1972,^[15] had a different model; it would distribute inexpensive scanners to schools and make profits from selling the test forms. As a result, many people came to think of all mark-sense forms (whether optically sensed or not) as scantron forms. Scantron operates as a subsidiary of M&F Worldwide(MFW)^[16] and provides testing and assessment systems and services and data collection and analysis services to educational institutions, businesses and government.

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In 1983, Westinghouse Learning Corporation was acquired by National Computer Systems (NCS). In 2000, NCS was acquired by Pearson Education, where the OMR technology formed the core of Pearson's Data Management group. In February 2008, M&F Worldwide purchased the Data Management group from Pearson; the group is now part of the Scantron brand.^[17]

OMR has been used in many situations as mentioned below. The use of OMR in inventory systems was a transition between punch cards and bar codes and is not used as much for this purpose.^[8] OMR is still used extensively for surveys and testing though.

Usage[edit]

The use of OMR is not limited to schools or data collection agencies; many businesses and health care agencies use OMR to streamline their data input processes and reduce input error. OMR, OCR, and ICR technologies all provide a means of data collection from paper forms. OMR may also be done using an OMR (discrete read head) scanner or an imaging scanner.^[18]

Applications[edit]

OMR betting form used in Japan Racing AssociationFukushima Racecourse, Japan.

Betting ticket using this form.

There are many other applications for OMR, for examples:

In the process of institutional research
Community surveys
Consumer surveys
Evaluations and feedback
Data compilation
Product evaluation
Time sheets and inventory counts
Membership subscription forms
Lotteries and voting
Geocoding (e.g. postal codes)
Mortgage loan, banking, and insurance applications

Field types[edit]

OMR has different fields to provide the format the questioner desires. These fields include:

Multiple, where there are several options but only one is chosen. For example, the form might ask for one of the options ABCDE; 12345; completely disagree, disagree, indifferent, agree, completely agree; or similar.
Grid: the bubbles or lines are set up in a grid format for the user to fill in a phone number, name, ID number and so on.
Add, total the answers to a single value
Boolean, answering yes or no to all that apply
Binary, answering yes or no to only one
Dotted lines fields, developed by Smartshoot OMR, allow border dropping like traditional color dropping.

Capabilities/requirements[edit]

In the past and presently, some OMR systems require special paper, special ink and a special input reader (Bergeron, 1998). This restricts the types of questions that can be asked and does not allow for much variability when the form is being input. Progress in OMR now allows users to create and print their own forms and use a scanner (preferably with a document feeder) to read the information.^[19] The user is able to arrange questions in a format that suits their needs while still being able to easily input the data.^[20] OMR systems approach one hundred percent accuracy and only take 5 milliseconds on average to recognize marks.^[19] Users can use squares, circles, ellipses and hexagons for the mark zone. The software can then be set to recognize filled in bubbles, crosses or check marks.

OMR can also be used for personal use. There are all-in-one printers in the market that will print the photos the user selects by filling in the bubbles for size and paper selection on an index sheet that has been printed. Once the sheet has been filled in, the individual places the sheet on the scanner to be scanned and the printer will print the photos according to the marks that were indicated.^{[citation needed]}

Disadvantages[edit]

There are also some disadvantages and limitations to OMR. If the user wants to gather large amounts of text, then OMR complicates the data collection.^[21] There is also the possibility of missing data in the scanning process, and incorrectly or unnumbered pages can lead to their being scanned in the wrong order. Also, unless safeguards are in place, a page could be rescanned, providing duplicate data and skewing the data.^[19]

As a result of the widespread adoption and ease of use of OMR, standardized examinations can consist primarily of multiple-choice questions, changing the nature of what is being tested.

References[edit]