Volume 2-issue-6-2009-2015

ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 6, June 2013
www.ijarcet.org
2009
Abstract— This paper presents a literature review on English
OCR techniques. English OCR system is compulsory to convert
numerous published books of English into editable computer
text files. Latest research in this area has been able to grown
some new methodologies to overcome the complexity of English
writing style. Still these algorithms have not been tested for
complete characters of English Alphabet. Hence, a system is
required which can handle all classes of English text and
identify characters among these classes.
Keywords— OCR, templates, english-alphabets,
alpha-numerics
I. INTRODUCTION
Optical Character Recognition [1] – [5] is a
process that can convert text, present in digital image, to
editable text. It allows a machine to recognize characters
through optical mechanisms. The output of the OCR should
ideally be same as input in formatting. The process involves
some pre-processing of the image file and then acquisition of
important knowledge about written text.
That knowledge or data can be used to recognize
characters. OCR [1] is becoming an important part of modern
research based computer applications. Especially with the
advent of Unicode and support of complex scripts on
personal computers, the importance of this application has
increased.
The current study is focused on exploration of
possible techniques to develop an OCR [2] system forEnglish
language when noise is present in the signal. A detailed
analysis of English writing system has been done in order to
understand the core challenges. Existing OCR systems are
also studied to know the latest research going on in this field.
The emphasis was on finding workable segmentation
technique and diacritic handling for English strings, and built
Sukhpreet Singh, Department of Computer Science, Yadawindra
College of Engineering, Punjabi University , Patiala,. Talwandi Sabo, Dist.
Bathinda (Punjab ) India, Mobile No:- 9780000405
a recognition module for these ligatures. The complete
methodology is proposed to develop an OCR system for
English and a testing application is also made. Test results are
reported and compared with the previous work done in this
area.
II. MOTIVATION
In the last years the trend to digitize (historic) paper
based documents such as books and newspapers, has
emerged. The aim is to preserve these documents and make
them fully accessible, searchable and processable in digital
form. Knowledge contained in paper based documents is
more valuable for today’s digital world when it is available in
digital form.
The first step towards transforming a paper based
archive into a digital archive is to scan the documents. The
next step is to apply an OCR (Optical Character Recognition)
process, meaning that the scanned image of each document
will be translated into machine processable text. Due to the
print quality of the documents and the error-prone pattern
matching techniques of the OCR process, OCR errors occur.
Modern OCR processors have character recognition rates up
to 99% on high quality documents. Assuming an average
word length of 5 characters, this still means that one out of 20
words is defect. Thus, at least 5% of all processed words will
contain OCR errors. On historic documents this error rate
will be even higher because the print quality is likely to be of
lower quality.
After finishing the OCR process several
post-processing steps are necessary depending on the
application, e.g. tagging the documents with meta-data
(author, year, etc.) or proof-reading the documents for
correcting OCR errors and spelling mistakes. Data which
contains spelling mistakes or OCR errors is difficult to
process. For example, a standard full-text search will not
retrieve misspelled versions of a query string. To fulfil
application’s demanding requirements toward zero errors, a
Optical Character Recognition Techniques:
A survey
Sukhpreet Singh

ISSN: 2278 – 1323
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2010
post-processing step to correct these errors is a very
important part of the post-processing chain.
A post-processing error correction system can be
manual, semi-automatic or fully automatic. A semi-automatic
post-correction system detects errors automatically and
proposes corrections to human correctors who then hainve to
choose the correct proposal. A fully-automatic
post-correction system does the detection and correction of
errors by its own. Because semi-automatic or manual
corrections require a lot of human effort and time,
fully-automatic systems become necessary to perform a full
correction.
III. DESIGN OF OCR
Various approaches used for the design of OCR systems are
discussed below:
Matrix Matching [6]: Matrix Matching converts each
character into a pattern within a matrix, and then compares
the pattern with an index of known characters. Its recognition
is strongest on monotype and uniform single column pages.
Fuzzy Logic [6]: Fuzzy logic is a multi-valued logic that
allows intermediate values to be defined between
conventional evaluations like yes/no, true/false, black/ white
etc. An attempt is made to attribute a more human-like way of
logical thinking in the programming of computers. Fuzzy
logic is used when answers do not have a distinct true or false
value and there is uncertainly involved.
Feature Extraction [3]-[6]: This method defines each
character by the presence or absence of key features,
including height, width, density, loops, lines, stems and other
character traits. Feature extraction is a perfect approach for
OCR of magazines, laser print and high quality images.
Structural Analysis [6]: Structural Analysis identifies
characters by examining their sub features- shape of the
image, sub-vertical and horizontal histograms. Its character
repair capability is great for low quality text and newsprints.
Neural Networks [6]: This strategy simulates the way
the human neural system works. It samples the pixels in each
image and matches them to a known index of character pixel
patterns. The ability to recognize characters through
abstraction is great for faxed documents and damaged text.
Neural networks are ideal for specific types of problems,
such as processing stock market data or finding trends in
graphical patterns.
1. Structure of OCR Systems
Diagrammatic representation of the structure of an OCR
system is given in figure 1.
Fig. 1: Diagrammatic Structure of the OCR System
(adapted from [6])
2. Stages in Design of OCR Systems
Various stages of OCR system design are given in figure 2.
Fig 2: Stages in OCR Design(adapted from [6])
3. Reasons for Poor Performance of OCR Systems

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2011
Existing OCR systems generally show poor performance for
documents like old books: print and paper quality inferior
due to aging, Copied Materials: documents like photocopies
or faxed documents, where print quality is inferior to the
original, Newspapers: generally printed on low quality paper
etc.
For such degraded documents, the system recognition
accuracy comes down to 80- 90%. But if we want to use the
OCR system for Banking and Corporate sector, this accuracy
rate is not up-to-mark.
IV. RELATED WORKS
Claudiu et al. (2011) [1] has investigated using
simple training data pre-processing gave us experts with
errors less correlated than those of different nets trained on
the same or bootstrapped data. Hence committees that simply
average the expert outputs considerably improve recognition
rates. Our committee-based classifiers of isolated
handwritten characters are the first on par with human
performance and can be used as basic building blocks of any
OCR system (all our results were achieved by software
running on powerful yet cheap gaming cards).
Georgios et al. (2010) [2] has presented a
methodology for off-line handwritten character recognition.
The proposed methodology relies on a new feature extraction
technique based on recursive subdivisions of the character
image so that the resulting sub-images at each iteration have
balanced (approximately equal) numbers of foreground
pixels, as far as this is possible. Feature extraction is followed
by a two-stage classification scheme based on the level of
granularity of the feature extraction method. Classes with
high values in the confusion matrix are merged at a certain
level and for each group of merged classes, granularity
features from the level that best distinguishes them are
employed. Two handwritten character databases (CEDAR
and CIL) as well as two handwritten digit databases (MNIST
and CEDAR) were used in order to demonstrate the
effectiveness of the proposed technique.
Sankaran et al. (2012) [3] has presented present a
novel recognition approach that results in a 15% decrease in
word error rate on heavily degraded Indian language
document images. OCRs have considerably good
performance on good quality documents, but fail easily in
presence of degradations. Also, classical OCR approaches
perform poorly over complex scripts such as those for Indian
languages. Sankaran et al. (2012) [3] addressed these issues
by proposing to recognize character n-gram images, which
are basically groupings of consecutive character/component
segments. Their approach was unique, since they use the
character n-grams as a primitive for recognition rather than
for post-processing.
By exploiting the additional context present in the
character n-gram images, we enable better disambiguation
Sbetween confusing characters in the recognition phase. The
labels obtained from recognizing the constituent n-grams are
then fused to obtain a label for the word that emitted them.
Their method is inherently robust to degradations such as
cuts and merges which are common in digital libraries of
scanned documents. We also present a reliable and scalable
scheme for recognizing character n-gram images. Tests on
English and Malayalam document images show considerable
improvement in recognition in the case of heavily degraded
documents.
Jawahar et al. (2012) [4] has propose a recognition scheme
for the Indian script of Devanagari. Recognition accuracy of
Devanagari script is not yet comparable to its Roman
counterparts. This is mainly due to the complexity of the
script, writing style etc. Our solution uses a Recurrent Neural
Network known as Bidirectional Long- Short Term Memory
(BLSTM). Our approach does not require word to character
segmentation, which is one of the most common reason for
high word error rate. Jawahar et al. (2012) [4] has reported a
reduction of more than 20% in word error rate and over 9%
reduction in character error rate while comparing with the
best available OCR system.
Zhang et al. (2012) [5] has discussed the misty, foggy, or
hazy weather conditions lead to image color distortion and
reduce the resolution and the contrast of the observed object
in outdoor scene acquisition. In order to detect and remove
haze, this article proposes a novel effective algorithm for
visibility enhancement from a single gray or color image.
Since it can be considered that the haze mainly concentrates
in one component of the multilayer image, the haze-free
image is reconstructed through haze layer estimation based
on the image filtering approach using both low-rank
technique and the overlap averaging scheme. By using
parallel analysis with Monte Carlo simulation from the
coarse atmospheric veil by the median filter, the refined
smooth haze layer is acquired with both less texture and
retaining depth changes. With the dark channel prior, the
normalized transmission coefficient is calculated to restore
fogless image. Experimental results show that the proposed
algorithm is a simpler and efficient method for clarity
improvement and contrast enhancement from a single foggy
image. Moreover, it can be comparable with the
state-of-the-art methods, and even has better results than
them.

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2012
Badawy, W. et al. (2012) [6] has discussed the
Automatic license plate recognition (ALPR) is the extraction
of vehicle license plate information from an image or a
sequence of images. The extracted information can be used
with or without a database in many applications, such as
electronic payment systems (toll payment, parking fee
payment), and freeway and arterial monitoring systems for
traffic surveillance. The ALPR uses either a color, black and
white, or infrared camera to take images.
Ntirogiannis et al. (2013) [7] has studied that the
document image binarization is of great importance in the
document image analysis and recognition pipeline since it
affects further stages of the recognition process. The
evaluation of a binarization method aids in studying its
algorithmic behavior, as well as verifying its effectiveness,
by providing qualitative and quantitative indication of its
performance. This paper addresses a pixel-based binarization
evaluation methodology for historical
handwritten/machine-printed document images. In the
proposed evaluation scheme, the recall and precision
evaluation measures are properly modified using a weighting
scheme that diminishes any potential evaluation bias.
Yang et al. (2012) [8] has proposed a novel
adaptive binarization method based on wavelet filter is
proposed in this paper, which shows comparable
performance to other similar methods and processes faster, so
that it is more suitable for real-time processing and applicable
for mobile devices. The proposed method is evaluated on
complex scene images of ICDAR 2005 Robust Reading
Competition, and experimental results provide a support for
our work.
Sumetphong et al. (2012) [9] has proposed a novel
technique for recognizing broken Thai characters found in
degraded Thai text documents by modeling it as a
set-partitioning problem (SPP). The technique searches for
the optimal set-partition of the connected components by
which each subset yields a reconstructed Thai character.
Given the non-linear nature of the objective function needed
for optimal set-partitioning, we design an algorithm we call
Heuristic Incremental Integer Programming (HIIP), that
employs integer programming (IP) with an incremental
approach using heuristics to hasten the convergence. To
generate corrected Thai words, we adopt a probabilistic
generative approach based a Thai dictionary corpus. The
proposed technique is applied successfully to a Thai
historical document and poor quality Thai fax document with
promising accuracy rates over 93%.
AlSalman et al. (2012) [10] has proposed that
Braille recognition is the ability to detect and recognize
Braille characters embossed on Braille document. The result
is used in several applications such as embossing, printing,
translating...etc. However, the performance of these
applications is affected by poor quality imaging due to
several factors such as scanner quality, scan resolution,
lighting, and type of embossed documents.
Mutholib et al. (2012) [11] has proposed that
Android platform has gained popularity in recent years in
terms of market share and number of available applications.
Android operating system is built on a modified Linux kernel
with built-in services such as email, web browser, and map
applications. In this paper, automatic number plate
recognition (ANPR) was designed and implemented on
Android mobile phone platform. First, the graphical user
interface (GUI) for capturing image using built-in camera
was developed to acquire car plate number in Malaysia.
Second, the preprocessing of raw image was done using
contrast enhancement, filtering, and straightening. Next,
an optical character recognition (OCR) using neural network
was utilized to extract texts and numbers. The proposed
ANPR algorithm was implemented and simulated using
Android SDK on a computer. The preliminary results showed
that our system is able to recognize most of the plate
characters by almost 88%. Future research includes
optimizing the system for mobile phone implementation with
limited CPU and memory resources, and geo-tagging of the
image using GPS coordinates and online database for various
mobile applications.
Chi et al. (2012) [12] has proposed that because of
the existence of possible carbon and seals, it's quite often that
images of financial documents such as Chinese bank checks
are suffered from bleed-through effects, which will affect the
performance of automatic financial document processing
such as seal verification and OCR. Chi et al. (2012) [12] has
presented an effective algorithm to deal with bleed-through
effects existing in the images of financial documents.
Double-sided images scanned simultaneously are used as
in-puts, and the bleed-through effect is detected and removed
after the registration of the recto and verso side images.
Ramakrishnan et al. (2012) [13] has proposed that
many machine learning algorithms rely on feature descriptors
to access information about image appearance. Using an
appropriate descriptor is therefore crucial for the algorithm to
succeed. Although domain- and task-specific feature
descriptors may result in excellent performance, they
currently have to be hand-crafted, a difficult and
time-consuming process. In contrast, general-purpose

ISSN: 2278 – 1323
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2013
descriptors (such as SIFT) are easy to apply and have proved
successful for a variety of tasks, including classification,
segmentation, and clustering. Unfortunately, most
general-purpose feature descriptors are targeted at natural
images and may perform poorly in document analysis tasks.
Ramakrishnan et al. (2012) [13] has proposed a method for
automatically learning feature descriptors tuned to a given
image domain. The method works by first extracting the
independent components of the images, and then building a
descriptor by pooling these components over multiple
overlapping regions. We test the proposed method on several
document analysis tasks and several datasets, and show that it
outperforms existing general-purpose feature descriptors.
Chattopadhyay et al. (2012) [14] has worked on a
low complexity video OCR system has been presented, that
can be deployed on an embedded platform. The novelty of
the proposed method is the use of low processing cycle and
memory and yet getting a recognition accuracy of 84.23%
which is higher than the usual video OCR recognition
accuracy. Moreover, the proposed method can recognize
about 180 characters on average per frame in 26.34
milliseconds.
Malakar et al.(2012)[15] has described that
extraction of text lines from document images is one of the
important steps in the process of an Optical Character
Recognition (OCR) system. In case of handwritten document
images, presence of skewed, touching or overlapping text
line(s) makes this process a real challenge to the researcher.
The present technique extracts 87.09% and 89.35% text lines
successfully from the said databases respectively.
Sankaran et al. (2012) [16] has proposed
a recognition scheme for the Indian script of
Devanagari. Recognition accuracy of Devanagari script is
not yet comparable to its Roman counterparts. This is mainly
due to the complexity of the script, writing style etc. Our
solution uses a Recurrent Neural Network known as
Bidirectional LongShort Term Memory (BLSTM). Our
approach does not require word to character segmentation,
which is one of the most common reason for high word error
rate. Sankaran et al. (2012) [16] has reported a reduction of
more than 20% in word error rate and over 9% reduction
in character error rate while comparing with the best
available OCR system.
Gur et al. (2012) [17] has discussed that
text recognition and retrieval is a well known problem.
Automated optical character recognition(OCR) tools do not
supply a complete solution and in most cases human
inspection is required. In this paper the authors suggest a
novel text recognition algorithm based on usage of fuzzy
logic rules relying on statistical data of the analyzed font. The
new approach combines letter statistics and correlation
coefficients in a set of fuzzy based rules, enabling
the recognition of distorted letters that may not be retrieved
otherwise. The authors focused on Rashi fonts associated
with commentaries of the Bible that are actually handwritten
calligraphy.
Devlin et al. (2012) [18] has discussed that when
performing handwriting recognition on natural language
text, the use of a word-level language model (LM) is known
to significantly improve recognition accuracy. The most
common type of language model, the n-gram model,
decomposes sentences into short, overlapping chunks. In this
paper, we propose a new type of language model which we
use in addition to the standard n-gram LM. Devlin et al.
(2012) [18]’s new model uses the likelihood score from a
statistical machine translation system as a reranking feature.
In general terms, we automatically translate each OCR
hypothesis into another language, and then create a feature
score based on how "difficult" it was to perform the
translation. Intuitively, the difficulty of translation correlates
with how well-formed the input sentence is. In an Arabic
handwriting recognition task, Devlin et al. (2012) [18] were
able to obtain a 0.4% absolute improvement to word error
rate (WER) on top of a powerful 5-gram LM.
Al-Khaffaf et al. (2012) [19] has presented the
current status of Decapod's English font reconstruction. The
Potrace algorithm and its parameters that affect glyph shape
are examined. The visual fidelity of Decapod's font
reconstruction is shown and compared to Adobe ClearScan.
The font reconstruction details of the two methods are
presented. The experiment demonstrates the capabilities of
the two methods in reconstructing the font for a synthetic
book typeset each time with one of six English fonts, three
serif and three sans-serif. For both typefaces, Decapod is able
to create a reusable TTF font that is embedded in the
generated PDF document.
Rhead et al. (2012) [20] has considered real world
UK number plates and relates these to ANPR. It considers
aspects of the relevant legislation and standards when
applying them to real world number plates. The varied
manufacturing techniques and varied specifications of
component parts are also noted. The varied fixing
methodologies and fixing locations are discussed as well as
the impact on image capture.
V. CONCLUSION
This paper has presented a related work on English OCR
techniques. Various available techniques are studied to find a

ISSN: 2278 – 1323
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2014
best technique. But is found that the techniques which
provide better results are slow in nature while fast techniques
mostly provide inefficient results. It is found that the OCR
techniques based on neural network provide more accurate
results than other techniques.
VI. REFERENCES
[1] Dan ClaudiuCires¸an and Ueli Meier and Luca
Maria Gambardella and JurgenSchmidhuber,
“Convolutional Neural Network Committees for
Handwritten Character Classification”, 2011
International Conference on Document Analysis
and Recognition, IEEE, 2011.
[2] GeorgiosVamvakas, Basilis Gatos, Stavros J.
Perantonis, “Handwritten character recognition
through two-stage foreground sub-sampling”
,Pattern Recognition, Volume 43, Issue 8, August
2010.
[3] Shrey Dutta, Naveen Sankaran, PramodSankar
K., C.V. Jawahar, “Robust Recognition of
Degraded Documents Using Character N-Grams”,
IEEE, 2012.
[4] Naveen Sankaran and C.V Jawahar,
“Recognition of Printed Devanagari Text Using
BLSTM Neural Network”, IEEE, 2012.
[5] Yong-Qin Zhang, Yu Ding, Jin-Sheng Xiao,
Jiaying Liu and Zongming Guo1, “Visibility
enhancement using an image filtering approach”,
Zhang et al. EURASIP Journal on Advances in
Signal Processing 2012.
[6] Badawy, W. "Automatic License Plate
Recognition (ALPR): A State of the Art Review."
(2012): 1-1.
[7] Ntirogiannis, Konstantinos, Basilis Gatos, and
IoannisPratikakis. "A Performance Evaluation
Methodology for Historical Document Image
Binarization." (2013): 1-1.
[8] Yang, Jufeng, Kai Wang, Jiaofeng Li, Jiao Jiao,
and Jing Xu. "A fast adaptive binarization method
for complex scene images." In Image Processing
(ICIP), 2012 19th IEEE International Conference
on, pp. 1889-1892. IEEE, 2012.
[9] Sumetphong, Chaivatna, and
SupachaiTangwongsan. "An Optimal Approach
towards Recognizing Broken Thai Characters in
OCR Systems." Digital Image Computing
Techniques and Applications (DICTA), 2012
International Conference on. IEEE, 2012.
[10] AlSalman, AbdulMalik, et al. "A novel
approach for Braille images segmentation."
Multimedia Computing and Systems (ICMCS),
2012 International Conference on. IEEE, 2012.
[11] Mutholib, Abdul, Teddy Surya Gunawan, and
Mira Kartiwi. "Design and implementation of
automatic number plate recognition on android
platform." Computer and Communication
Engineering (ICCCE), 2012 International
Conference on. IEEE, 2012.
[12] Chi, Bingyu, and Youbin Chen. "Reduction of
Bleed-through Effect in Images of Chinese Bank
Items." Frontiers in Handwriting Recognition
(ICFHR), 2012 International Conference on.
IEEE, 2012.
[13] Ramakrishnan, Kandan, and Evgeniy Bart.
"Learning domain-specific feature descriptors for
document images." Document Analysis Systems
(DAS), 2012 10th IAPR International Workshop
on. IEEE, 2012.
[14] Chattopadhyay, T., Ruchika Jain, and Bidyut
B. Chaudhuri. "A novel low complexity TV video
OCR system." Pattern Recognition (ICPR), 2012
21st International Conference on. IEEE, 2012.
[15] Malakar, Samir, et al. "Text line extraction
from handwritten document pages using spiral run
length smearing algorithm." Communications,
Devices and Intelligent Systems (CODIS), 2012
International Conference on. IEEE, 2012.
[16] Sankaran, Naveen, and C. V. Jawahar.
"Recognition of printed Devanagari text using
BLSTM Neural Network." Pattern Recognition
(ICPR), 2012 21st International Conference on.
IEEE, 2012.
[17] Gur, Eran, and ZeevZelavsky. "Retrieval of
Rashi Semi-Cursive Handwriting via Fuzzy
Logic." Frontiers in Handwriting Recognition
(ICFHR), 2012 International Conference on.
IEEE, 2012.
[18] Devlin, Jacob, "Statistical Machine
Translation as a Language Model for Handwriting
Recognition." Frontiers in Handwriting
Recognition (ICFHR), 2012 International
[19] Al-Khaffaf, Hasan SM, et al. "On the
performance of Decapod's digital font
reconstruction." Pattern Recognition (ICPR),
2012 21st International Conference on. IEEE,
2012.
[20] Rhead, Mke, "Accuracy of automatic number
plate recognition (ANPR) and real world UK
number plate problems." Security Technology
(ICCST), 2012 IEEE International Carnahan

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2015
BIBLIOGRAPHY
Sukhpreet Singh received the
B.Tech degree in Computer Science Engineering
from the Baba Banda Singh Bahadur College of
Engineering, Punjab Technical University
Jalandhar( Punjab) in 2010, and pursuing M.Tech
degree in Yadawindra College of Engineering
University, Punjabi University, Patiala (Punjab).
Currently, he is doing his thesis work on a new
Optical Character Recognition (OCR) technique.
Her topic of interest is Optical Character
Recognition (OCR).

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