An Overview and Applications of Optical Character Recognition

Abstract

Optical character recognition, usually abbreviated to OCR, is the mechanical or electronic conversion of
scanned or photographed images of typewritten or printed text into machine-encoded/computer-readable text. It is widely used as a form of data entry from some sort of original paper data source, whether passport
documents, invoices, bank statement, receipts, business card, mail, or any number of printed records. 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 machine translation, text-to-speech, key data extraction and text mining. OCR is a field of research in pattern recognition, artificial intelligence and computer vision. Optical Character Recognition or OCR is the electronic translation of handwritten, typewritten or printed text into machine translated images. It is widely used to recognize and search text from electronic documents or to publish the text on a website [1]. A large number of research papers and reports have already been published on this topic. The paper presents introduction, major research work and applications of Optical Character Recognition in various fields. At the first introduction of OCR will be discussed and then some points will be stressed on the major research works that have made a great impact in character recognition. And finally the most important applications of OCR will be covered and then conclusion.

Full text

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AN OVERVIEW AND APPLICATIONS OF OPTICAL
CHARACTER RECOGNITION
Ali Mir Arif Mir Asif 1, Shaikh Abdul Hannan2, Yusuf Perwej3,
Mane Arjun Vithalrao4
1Institute of Management Studies & I.T., Aurangabad (M.S.)(India)
2,3Department of CS & IT, Albaha University, Albaha, (Saudi Arabia)
4MGM’s Dr. G. Y. Pathrikars College of C. S. & I.T., Aurangabad (M.S.) (India)
ABSTRACT
Optical character recognition, usually abbreviated to OCR, is the mechanical or electronic conversion of
scanned or photographed images of typewritten or printed text into machine-encoded/computer-readable text. It
is widely used as a form of data entry from some sort of original paper data source, whether passport
documents, invoices, bank statement, receipts, business card, mail, or any number of printed records. 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 machine translation, text-to-speech, key
data extraction and text mining. OCR is a field of research in pattern recognition, artificial
intelligence and computer vision. Optical Character Recognition or OCR is the electronic translation of
handwritten, typewritten or printed text into machine translated images. It is widely used to recognize and
search text from electronic documents or to publish the text on a website [1]. A large number of research
papers and reports have already been published on this topic. The paper presents introduction, major research
work and applications of Optical Character Recognition in various fields. At the first introduction of OCR will
be discussed and then some points will be stressed on the major research works that have made a great impact
in character recognition. And finally the most important applications of OCR will be covered and then
conclusion.
Keywords: Calligraphy, Hand Written Characters, Optical Character Recognition (OCR),
Practical Applications, Text.
I INTRODUCTION
The paper presents introduction, major research work and applications of Optical Character Recognition in
various fields. At the first introduction of OCR will be discussed and then some points will be stressed on the
major research works that have made a great impact in character recognition. And finally the most important
applications of OCR will be covered and then conclusion. OCR is generally an "offline" process, which analyzes
a static document. Handwriting movement analysis can be used as input to handwriting recognition [2]. Instead

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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". Character recognition techniques associate a symbolic identity with the image of a character. This
problem of replication of human functions by machines (computers) involves the recognition of both machine
printed and handprinted/cursive-written characters [3]. Highlight in 1950’s [4], applied throughout the spectrum
of industries resulting into revolutionizing the document management process. Optical Character Recognition or
OCR has enabled scanned documents to become more than just image files, turning into fully searchable
documents with text content recognized by computers. Optical Character Recognition extracts the relevant
information and automatically enters it into electronic database instead of the conventional way of manually
retyping the text. Optical Character Recognition is a vast field with a number of varied applications such as
invoice imaging, legal industry, banking, health care industry, etc. OCR is also widely used in many other fields
like Captcha, Institutional repositories and digital libraries, Optical Music Recognition without any human
correction or human effort, Automatic number plate recognition and Handwritten Recognition [5]-[10].
Early optical character recognition could be traced to activity around two issues: expanding 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. Around the same time, 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. 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. 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 [11]. 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 CCD flatbed 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. 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 paper legal and news documents
onto its nascent online databases [12]. 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 has been made available online as a service
(WebOCR), in a cloud computing environment, and in mobile applications like real-time translation of foreign-

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language signs on a smartphone. 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.
The origins of character recognition [13]-[15]
can be
found in
1870 when Carey invented the retina scanner, that is an image transmission system using a mosaic of
photocells, and later in 1890 when Nipkow invented
the sequential scanner which was a major break-
through both for modern television and reading machines. However, character recognition first appeared as
an aid to the visually handicapped and the first
successful attempts were made by the Russian scientist
Tyurin in 1900. The next attempts that have been
reported are the Fourier d’Albe’s Optophone of 1912
and
Thomas’ tactile ―relief’ device of 1926.
The modern version of OCR [16] appeared in the
middle l940s with the development of the digital
computer.
For the first time, OCR was realised as a
data processing approach, with particular application to the
business world. From that perspective, David
Shepard, founder of the Intelligent Machine Research
Co. can
be considered as the pioneer of the development and building of commercial OCR equipment.
Character
recognition is better known as optical character recognition (OCR) since it deals with recognition of optically
processed characters rather than magnetically processed [17] ones. Though the origin of character recognition
can be found as early as 1870, it first appeared as an aid to the visually handicapped, and the first successful
attempt was made by the Russian scientist Tyurin in 1900 [18]. The modern version of OCR appeared in the
middle of the 1940s with the development of the digital computers. Thenceforth it was realized as a data
processing approach with application to the business world. The principal motivation for the development of
OCR systems is the need to cope with the enormous flood of paper such as bank cheques, commercial forms,
government records, credit card imprints and mail sorting generated by the expanding technological society.
OCR machines have been commercially available since the middle of the 1950s. Since then extensive research
has been carried out and a large number of technical papers and reports have been published by various
researchers in the area of character recognition. Several books have been published on optical character
recognition [19]-[27]. Also special issues and reports on the topic have repeatedly appeared in the proceedings
of the International Joint Conferences on Pattern Recognition and of the International System, Man and
Cybernetics Conferences- Research works also appear in various other Conferences such as British Conferences
on Pattern Recognition, and The Scandinavian Conferences on Image Analysis. State of the art reports on
character recognition (research have been presented by Nagy [28], Harmon [29]), Stallings [30] Suen et al. [31),
Mori et al. [32] Mantas [18]), Davis and Yall [33], and Chatterji [34].
II MAJOR RESEARCH WORK IN OCR
A notable early attempt in the area of character recognition research is by Grimsdale et al. [35] in 1958. In their
method, the input character pattern obtained by a flying spot scanner is described in terms of length and slope of
straight line segments and length and curvature of curved segments. The description is compared with that of the
prototype stored in the computer in order to reach the proper decision about the identity of the unknown
character. Another important work is the analysis-by-synthesis method suggested by Eden [36], [37] at M.I.T.

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He put forward the idea that all Latin script characters can be formed by 18 strokes, which in turn can be
generated from a subset of 4 strokes, namely, hump, bar, hook, and loop. Some of the examples of the works in
this directions are those by Blesser et al. [38], Cox et al. [39], Shillman et al. [40], Yoshida and Eden [41] and
Berthod [42]. Blesser et al. proposed a theoretical approach based on phenomenological attributes. Cox et al.
presented two main groups of grammar-like rules to deal with variability in type fonts. Three experimental
techniques for studying ambiguous characters and for investigating relationship between physical and functional
attributes were suggested by Shillman et al. Yoshida and Eden proposed a Chinese character recognition system
which employs a generative process to extract a stroke sequence from the input pattern, and a look up dictionary
of strokes to effect recognition. Berthod utilized Eden's primitives for cursive script analysis.
In the sixties, Narasimhan suggested labeling schemata for syntactic description of pictures [43], and a syntax
directed interpretation of classes of pictures [44]. In another work [45], he proposed a recognition technique
based on description and generation. Using primitives and relations, he described a specification language for
handprinted FORTRAN character recognition. Later, Narasimhan and Reddy [46] put forward a syntax-aided
recognition scheme, wherein they incorporated in the decision rule some flexibility required for the satisfactory
performance of a recognition system. The authors expressed the views that the rule currently in use must be
refined, modified, and augmented continuously on the basis of the experience and other relevant knowledge
acquired. Pavlidis and Ali [47] and Ali and Pavlidis [49] utilized split-and-merge algorithm [48] for the
polygonal approximation of characters for numeral recognition. A feature generation technique for syntactic
pattern recognition by approximating character boundary by polygons and then decomposing on the basis of
concavity is suggested by Feng and Pavlidis [50] in 1974. Major research activities in character recognition are
now centered about the recognition of handprinted Chinese characters, which was once considered to be a very
hard problem and regarded as one of the ultimate goals of character recognition research. In 1966, Casey and
Nagy [51] at IBM presented one of the first attempts at Chinese character recognition.
In late 1970, Agui and Nagahashiu [52] suggested a description method for handprinted Chinese character
recognition. In their technique, a Chinese character is represented by partial patterns using three relations,
namely concatenate, cross and near. The relations of relative location among partial patterns are used for
categorization of the partial patterns. In 1980, Arakawa [53] suggested an on-line hand- written character
recognition system for Japanese characters. Fourier coefficients of pen-point movement loci relating to strokes
are utilized as feature vectors. A modified relaxation technique, incorporating the knowledge about the Chinese
characters into the training system to reduce computational load is suggested by Leung et al. [54]. Finally Yong
[55] suggested recognition via neural networks for achieving fast recognition of handprinted Chinese characters.
Not many attempts have been carried out on the recognition of Indian character sets. However, some major
works are reported on Devanagari (an Indian script used for writing Sanskrit, Hindi and some other languages
[56-60] and Tamil [61-65] character recognition. Some attempts are also reported on Brahmi (a script widely
used all over India during third century BC) [62], Telugu [66] and Bengali [67] characters. Some of the
important character recognition research of the early eighties are those by Tanaka et al. [68], Sarvarayudu and
Sethi [69], Shridhar and Badreldin [70] [71]; Sato et al. [72] and Evangelisti [73]. A brief description of them is
given in the following.

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A large amount of research work has been carried out in the mid eighties and after. A few of them are reviewed
here. They include contextual post processing by Nagy et al. [74] and Sinha [60] word/script recognition by
Almuallim and Yamaguchi [75], El-sheikh and Guindi [76], Hull [77], Aoki and Yamaya [78] Wong and
Fallside [79], and Shrihari and Bozinovic [80], separation of connected characters by Tampi and Chetlur [81]
and Ting and Ward [82], numeral recognition by Lam and Suen [83], and Baptista and Kulkarni [84], multifont
learning by Cannat et al. [85] [86], learning by experience by Malyan and Sunthankar [87] Pitman's shorthand
recognition by Leedham and Downton [88] 89], pattern description and generation technique by Nagahashi and
Nakatsuyama [90], description aided recognition by Harjinder [91] and pre-classification and recognition using
Walsh transform by Huang and Lung [92]. Other important works include the work by Wolberg [93] who
suggested a syntactic omni-font system that recognizes a wide range of fonts including hand-printed characters;
on performance testing of mixed font variable size character recognizers by Lam and Baird [94], about the
vectorizer and feature extractor for the document reader suggested by Pavlidis [95], and on the guide lines for
designing feature vectors for use with large character sets given by Hagita and Masuda [96]. The scopes of all
the above attempts were limited because they use simple features which do not exactly or directly reflect the
structural details of the characters. They cannot represent the varying structural complexities of different
alphabet sets. Moreover, with such simple features the automatic design problem will be easier to handle.
Recently, the authors [97] have suggested an automated approach to the design of recognizers suitable for
structurally different character sets. The approach is somewhat similar to that of Kami's [98]. However, a
flexible and unified/general feature representation is employed to take care of the controlled incorporation of
structural details (to describe various character classes) depending upon the complexity of an alphabet set.
III APPLICATIONS OF OCR
Optical character recognition has been applied to a number of applications. Some of the literatures covering
these are languages other than English, namely, Latin, Cyrillic, Arabic, Hebrew, Indic, Bengali (Bangla),
Devanagari, Tamil, Chinese, Japanese, Korean, etc. Highlight in 1950’s [99], applied throughout the spectrum of
industries resulting into revolutionizing the document management process. Optical Character Recognition or
OCR has enabled scanned documents to become more than just image files, turning into fully searchable
documents with text content recognized by computers. Optical Character Recognition extracts the relevant
information and automatically enters it into electronic database instead of the conventional way of manually
retyping the text. Optical Character Recognition is a vast field with a number of varied applications such as
Practical applications, Invoice imaging, Legal industry, Banking, Healthcare, etc. OCR is also widely used in
many other fields like Captcha, Institutional repositories and digital libraries, Optical Music Recognition without
any human correction or human effort, Automatic number plate recognition, Handwritten Recognition and Other
Industries. Some of them have been explained below:
3.1 Practical Applications
In recent years, OCR (Optical Character Recognition) technology has been applied throughout the entire
spectrum of industries, revolutionizing the document management process. OCR has enabled scanned documents

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to become more than just image files, turning into fully searchable documents [100] with text content that is
recognized by computers. With the help of OCR, people no longer need to manually retype important documents
when entering them into electronic databases. Instead, OCR extracts relevant information and enters it
automatically. The result is accurate, efficient information processing in less time.
3.2 Invoice Imaging
It is widely used in many businesses applications to keep track of financial records and prevent a backlog of
payments [100] from piling up. In government agencies and independent organizations, OCR simplifies data
collection and analysis, among other processes. As the technology continues to develop, more and more
applications are found for OCR technology, including increased use of handwriting [101] recognition.
Furthermore, other technologies related to OCR, such as barcode recognition, are used daily in retail and other
industries.
3.3 Legal Industry
In the legal industry, there has also been a significant movement to digitize paper documents. In order to save
space and eliminate the need to sift through boxes of paper files, documents are being scanned and entered into
computer databases. OCR further simplifies the process by making documents text-searchable, so that they are
easier to locate and work with once in the database. Legal professionals now have fast, easy access to a huge
library of documents in electronic format, which they can find simply by typing in a few keywords [100] [101].
3.4 Banking
The uses of OCR vary across different fields. One widely known application is in banking, where OCR is used to
process checks without human involvement. A check can be inserted into a machine, the writing on it is scanned
instantly, and the correct amount of money is transferred. This technology [100] [101] has nearly been perfected
for printed checks, and is fairly accurate for handwritten checks as well, though it occasionally requires manual
confirmation. Overall, this reduces wait times in many banks.
3.5 Healthcare
It Healthcare has also seen an increase in the use of OCR technology to process paperwork. Healthcare
professionals always have to deal with large volumes of forms for each patient, including insurance forms as well
as general health forms. To keep up with all of this information, it is useful to input relevant data into an
electronic database that can be accessed as necessary. Form processing [100] [101] tools, powered by OCR, are
able to extract information from forms and put it into databases, so that every patient's data is promptly recorded.
As a result, healthcare providers can focus on delivering the best possible service to every patient.
3.6 Captcha
CAPTCHA is a program that can generate and grade tests that human can pass but current computers
programmers’ cannot. Hacking is a serious threat to internet usage. Now a day’s most of the human activities
like economic transactions, admission for education, registrations, travel bookings etc are carried out through

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internet and all this requires a password which is misused by hackers. They create programs to like dictionary
attacks and automatic false enrolments which lead to waste of memory and resources of website. Dictionary
attack is attack against password authenticated systems where a hacker [102] writes a program to repeatedly try
different passwords like from a dictionary of most common passwords. In CAPTCHA, an image consisting of
series of letters of number is generated which is obscured by image distortion techniques, size and font variation,
distracting backgrounds, random segments, highlights and noise in the image. This system can be used to remove
this noise and segment the image to make the image tractable for the OCR (Optical Character Recognition)
systems.
3.7 Institutional Repositories and Digital Libraries
Institutional repositories are digital collections of the outputs created within a university or research institution.
It is an online locale of intellectual data of an institution, especially a research institution where it is collected,
preserved and aired. It helps to open up the outputs of an institution and give it visibility and more impact on
worldwide level. Enables and encourages interdisciplinary approaches to research and facilitates the
development [103] and sharing of digital teaching materials and aids. It is basically a collection of peer reviewed
journal articles, conference proceedings, research data, monographs, books, theses and dissertations and
presentations. Their first role is to provide the Open Access literature. Practical implementation of this includes
setting up a system which consists of scanner which scans the documents. This scanned document is then fed as
an input to an Optical Character Recognition system where information is acquired and retained in digitized
form.
3.8 Optical Music Recognition
Automated learning system extract information from images and is part of major researches. Optical music
recognition (OMR) born in 1950’s is a developed field and initially was aimed towards recognizing printed
sheets [104] which can be edited into playable form with the help of electronic and electrochemical methods. An
OMR system has many applications like processing of different classes of music, large scale digitization of
musical data and also it can be used for diversity in musical notation. Image enhancement and segmentation is
the basic step and hence the paper focuses on it.
3.9 Automatic Number Recognition
Automatic number plate recognition is used as a mass surveillance technique making use of optical character
recognition on images to identify vehicle registration plates. ANPR has also been made to store the images
captured by the cameras including the numbers captured from license [105] plate. ANPR technology own to
plate variation from place to place as it is a region specific technology. They are used by various police forces
and as a method of electronic toll collection on pay-per-use roads and cataloging the movements of traffic or
individuals.

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3.10 Handwriting Recognition
Handwriting recognition is the ability of a computer to receive and interpret intelligible handwritten input from
sources such as paper documents, photographs, touch-screens and other devices. The image of the written text
may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent
word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-
based computer screen surface [106].
3.11 Other Industries
OCR is widely used in many other fields, including education, finance, and government agencies. OCR has made
countless texts available online, saving money for students and allowing knowledge to be shared. Invoice
imaging applications are used in many businesses to keep track of financial records and prevent a backlog of
payments from piling up. In government agencies and independent organizations, OCR simplifies data collection
and analysis, among other processes. As the technology continues to develop, more and more applications are
found for OCR technology [100], including increased use of handwriting recognition. Furthermore, other
technologies related to OCR, such as barcode recognition, are used daily in retail and other industries. To learn
more about OCR solutions for your office, you can download a free trial of Maestro Recognition Server,
CVISION's OCR toolkit, or Trapeze, our automated form-processing solution.
IV CONCLUSION
Nowadays, a lot of documents are produced in paper form but it is obvious, that automatic data recognition
systems are very popular. The document is repeatedly copied and changed during subsequent processing steps,
so it exists in many different copies. In some applications they can successfully help humans, but in some cases
they are useless. Though researchers have suggested various sophisticated ideas and techniques to deal with the
recognition of unconstrained and connected characters, practical OCR systems suffer from a lack of such
characteristics. It is because of the claims made by the researchers are not adequately substantiated by exposure
of the systems into real working environments/conditions the lack of practical feasibility of such advanced
techniques with the available hardware from an economical viewpoint. From these constraints and the lack of
performances it can be concluded that the ability to read text by machines with the same fluency as the human
remains an unachieved goal, though a great amount of effort has already been expended on the subject. By
handwritten character recognition one means the recognition of single and unconstrained hand drawn characters,
i.e. numerals, upper-case and lowercase characters of a particular alphabet. However, the frontiers of character
recognition have now moved to the recognition of cursive script that is the recognition of characters which may
be connected or written in calligraphy.
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