Design and implementation of optical character recognition using template mat...eSAT Journals
Abstract
Optical character recognition (OCR) is an efficient way of converting scanned image into machine code which can further edit. There are variety of methods have been implemented in the field of character recognition. This paper proposes Optical character recognition by using Template Matching. The templates formed, having variety of fonts and size .In this proposed system, Image pre-processing, Feature extraction and classification algorithms have been implemented so as to build an excellent character recognition technique for different scripts .Result of this approach is also discussed in this paper. This system is implemented in Matlab.
Keywords- OCR, Feature Extraction, Classification
I have presented the power point presentation on Basics of the optical character recognition. Here i have focused to discuss about hoe OCR is used in scanning process and can it be used for document scanning and its uses.
Design and implementation of optical character recognition using template mat...eSAT Journals
Abstract
Optical character recognition (OCR) is an efficient way of converting scanned image into machine code which can further edit. There are variety of methods have been implemented in the field of character recognition. This paper proposes Optical character recognition by using Template Matching. The templates formed, having variety of fonts and size .In this proposed system, Image pre-processing, Feature extraction and classification algorithms have been implemented so as to build an excellent character recognition technique for different scripts .Result of this approach is also discussed in this paper. This system is implemented in Matlab.
Keywords- OCR, Feature Extraction, Classification
I have presented the power point presentation on Basics of the optical character recognition. Here i have focused to discuss about hoe OCR is used in scanning process and can it be used for document scanning and its uses.
Presentation on the New Technology based on the recognition of letters that would be available on Soft and Hard copy both and allow all the format in Soft Copy. Optical character Recognition based on the recognition of letters with all the existing languages.
A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUESijcsitcejournal
Optical Character Recognition (OCR) is the process which enables a system to without human intervention
identifies the scripts or alphabets written into the users’ verbal communication. Optical Character
identification has grown to be individual of the mainly flourishing applications of knowledge in the field of
pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this
paper we resolve and examine the hypothetical and numerical models of Optical Character Identification.
The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR)
techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are
in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or
direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine
categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both
MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on
Optical Character detection or recognition has shown that the In Handwritten text there is no limitation
lying on the script technique. Hand written correspondence is complicated to be familiar through due to
diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of
approaches of Optical Character Identification is discussed here all along through their achievement.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Optical Character Recognition Using PythonYogeshIJTSRD
Optical Character Recognition is a process of classifying optical patterns with respect to alphanumeric or other characters. It also includes segmentation, feature extraction and classification. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with. representation learning The idea of the project is to extract text from image using Deep Learning by OCR Ponvizhi. U | Ramya. P | Ramya. R "Optical Character Recognition Using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41099.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/41099/optical-character-recognition-using-python/ponvizhi-u
This research tries to find out amethodology through which any data from the daily-use printed bills and invoices can be extracted. The data from these bills or invoices can be used extensively later on –such as machine learning or statistical analysis. This research focuses on extraction of final bill-amount, itinerary, date and similar data from bills and invoices as they encapsulate an ample amount of information about the users purchases, likes or dislikes etc. Optical Character Recognition (OCR) technology is a system that provides a full alphanumeric recognition of printed or handwritten characters from images. Initially, OpenCV has been used to detect the bill or invoice from the image and filter out the unnecessary noise from the image. Then intermediate image is passed for further processing using Tesseract OCR engine, which is an optical character recognition engine. Tesseract intends to apply Text Segmentation in order to extract written text in various fonts and languages. Our methodology proves to be highly accurate while tested on a variety of input images of bills and invoices.
Presented by Rida Khan,Safa Aamir & Shehrbano Lakhanie, this is a very precise the powerful presentation on working principals of OCR, OMR and Track Ball.
The Presentation is about Optical Character Recognition, Talks about high technology devices such as Bar-code scanner, book readers, Image to Tech Converter and paper Scanners
If your organization is considering or has deployed optical character recognition software for your in-house utility bill data processing needs, you may be doing more work than you need to.
Handwritten Text Recognition and Digital Text Conversionijtsrd
Sometimes it is extremely difficult to secure handwritten documents in the real world. While doing so, we may encounter many problems such as misplacing the documents, unavailability of access from anywhere, physical damage, etc. So, to keep the information secure, we convert that information into digital format to address all the above mentioned problems. The main aim of our application is to recognize hand written text and display it in digital text format. Image processing is very significant process for data analysis these days. In image processing, the visible text from the real world as input must be processed precisely in order to produce the same information as output with accuracy. To do this, the text present in the image must be recognized by the system accurately. The proposed system aims at achieving these results. The process goes in this way The image which contains the handwritten text is fed to the system is passed into neural network which recognizes the handwritten text present in the image and displays it in the form of digital text. This can be used for many purposes such as copying the digital text for using it elsewhere, producing formal documents and can also be used as input for data processing. Using this process, we can store the information in a secure way, we can access the information from anywhere or at any time and there is no scope for physical damage as the information is in digital format. Mr. B. Ravinder Reddy | J. Nandini | P. Sowmya | Y. Sathwik ""Handwritten Text Recognition and Digital Text Conversion"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23508.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23508/handwritten-text-recognition-and-digital-text-conversion/mr-b-ravinder-reddy
Optical Character Recognition: the What, Why, and Howmackenziekbrooks
Delivered by Mackenzie Brooks and Alston Cobourn to Washington and Lee University. This presentation explains what OCR is, gives a variety of use cases, and covers the types of tools available.
Smart Assistant for Blind Humans using Rashberry PIijtsrd
An OCR (Optical Character Recognition) system which is a branch of computer vision and in turn a sub-class of Artificial Intelligence. Optical character recognition is the translation of optically scanned bitmaps of printed or hand-written text into audio output by using of Raspberry pi. OCRs developed for many world languages are already under efficient use. This method extracts moving object region by a mixture-of-Gaussians-based background subtraction method. A text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object, a text localization and Tesseract algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Adaboost model. Text characters in the localized text regions are then binaries and recognized by off-the-shelf optical character recognition software. The recognized text codes are output to blind users in speech. Performance of the proposed text localization algorithm. As the recognition process is completed, the character codes in the text file are processed using Raspberry pi device on which recognize character using Tesseract algorithm and python programming, the audio output is listed. Abish Raj. M. S | Manoj Kumar. A. S | Murali. V"Smart Assistant for Blind Humans using Rashberry PI" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11498.pdf http://www.ijtsrd.com/computer-science/embedded-system/11498/smart-assistant-for-blind-humans-using-rashberry-pi/abish-raj-m-s
Presentation on the New Technology based on the recognition of letters that would be available on Soft and Hard copy both and allow all the format in Soft Copy. Optical character Recognition based on the recognition of letters with all the existing languages.
A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUESijcsitcejournal
Optical Character Recognition (OCR) is the process which enables a system to without human intervention
identifies the scripts or alphabets written into the users’ verbal communication. Optical Character
identification has grown to be individual of the mainly flourishing applications of knowledge in the field of
pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this
paper we resolve and examine the hypothetical and numerical models of Optical Character Identification.
The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR)
techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are
in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or
direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine
categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both
MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on
Optical Character detection or recognition has shown that the In Handwritten text there is no limitation
lying on the script technique. Hand written correspondence is complicated to be familiar through due to
diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of
approaches of Optical Character Identification is discussed here all along through their achievement.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Optical Character Recognition Using PythonYogeshIJTSRD
Optical Character Recognition is a process of classifying optical patterns with respect to alphanumeric or other characters. It also includes segmentation, feature extraction and classification. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with. representation learning The idea of the project is to extract text from image using Deep Learning by OCR Ponvizhi. U | Ramya. P | Ramya. R "Optical Character Recognition Using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41099.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/41099/optical-character-recognition-using-python/ponvizhi-u
This research tries to find out amethodology through which any data from the daily-use printed bills and invoices can be extracted. The data from these bills or invoices can be used extensively later on –such as machine learning or statistical analysis. This research focuses on extraction of final bill-amount, itinerary, date and similar data from bills and invoices as they encapsulate an ample amount of information about the users purchases, likes or dislikes etc. Optical Character Recognition (OCR) technology is a system that provides a full alphanumeric recognition of printed or handwritten characters from images. Initially, OpenCV has been used to detect the bill or invoice from the image and filter out the unnecessary noise from the image. Then intermediate image is passed for further processing using Tesseract OCR engine, which is an optical character recognition engine. Tesseract intends to apply Text Segmentation in order to extract written text in various fonts and languages. Our methodology proves to be highly accurate while tested on a variety of input images of bills and invoices.
Presented by Rida Khan,Safa Aamir & Shehrbano Lakhanie, this is a very precise the powerful presentation on working principals of OCR, OMR and Track Ball.
The Presentation is about Optical Character Recognition, Talks about high technology devices such as Bar-code scanner, book readers, Image to Tech Converter and paper Scanners
If your organization is considering or has deployed optical character recognition software for your in-house utility bill data processing needs, you may be doing more work than you need to.
Handwritten Text Recognition and Digital Text Conversionijtsrd
Sometimes it is extremely difficult to secure handwritten documents in the real world. While doing so, we may encounter many problems such as misplacing the documents, unavailability of access from anywhere, physical damage, etc. So, to keep the information secure, we convert that information into digital format to address all the above mentioned problems. The main aim of our application is to recognize hand written text and display it in digital text format. Image processing is very significant process for data analysis these days. In image processing, the visible text from the real world as input must be processed precisely in order to produce the same information as output with accuracy. To do this, the text present in the image must be recognized by the system accurately. The proposed system aims at achieving these results. The process goes in this way The image which contains the handwritten text is fed to the system is passed into neural network which recognizes the handwritten text present in the image and displays it in the form of digital text. This can be used for many purposes such as copying the digital text for using it elsewhere, producing formal documents and can also be used as input for data processing. Using this process, we can store the information in a secure way, we can access the information from anywhere or at any time and there is no scope for physical damage as the information is in digital format. Mr. B. Ravinder Reddy | J. Nandini | P. Sowmya | Y. Sathwik ""Handwritten Text Recognition and Digital Text Conversion"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23508.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23508/handwritten-text-recognition-and-digital-text-conversion/mr-b-ravinder-reddy
Optical Character Recognition: the What, Why, and Howmackenziekbrooks
Delivered by Mackenzie Brooks and Alston Cobourn to Washington and Lee University. This presentation explains what OCR is, gives a variety of use cases, and covers the types of tools available.
Smart Assistant for Blind Humans using Rashberry PIijtsrd
An OCR (Optical Character Recognition) system which is a branch of computer vision and in turn a sub-class of Artificial Intelligence. Optical character recognition is the translation of optically scanned bitmaps of printed or hand-written text into audio output by using of Raspberry pi. OCRs developed for many world languages are already under efficient use. This method extracts moving object region by a mixture-of-Gaussians-based background subtraction method. A text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object, a text localization and Tesseract algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Adaboost model. Text characters in the localized text regions are then binaries and recognized by off-the-shelf optical character recognition software. The recognized text codes are output to blind users in speech. Performance of the proposed text localization algorithm. As the recognition process is completed, the character codes in the text file are processed using Raspberry pi device on which recognize character using Tesseract algorithm and python programming, the audio output is listed. Abish Raj. M. S | Manoj Kumar. A. S | Murali. V"Smart Assistant for Blind Humans using Rashberry PI" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11498.pdf http://www.ijtsrd.com/computer-science/embedded-system/11498/smart-assistant-for-blind-humans-using-rashberry-pi/abish-raj-m-s
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Assistive Examination System for Visually ImpairedEditor IJCATR
This paper presents a design of voice enabled examination system which can be used by the visually challenged students.
The system uses Text-to-Speech (TTS) and Speech-to-Text (STT) technology. The text-to-speech and speech-to-text web based
academic testing software would provide an interaction for blind students to enhance their educational experiences by providing them
with a tool to give the exams. This system will aid the differently-abled to appear for online tests and enable them to come at par with
the other students. This system can also be used by students with learning disabilities or by people who wish to take the examination in
a combined auditory and visual way.
Optical character recognition (OCR) is a process of transforming or converting machine-printed text, into digital ASCII text so that it can be recognized and utilized by computers, tablets, and other devices. It can be used in digitizing machine-printed text from scanned paper documents, old books, microfilm, microfiche, drawings, maps, and other hard copy sources.
The entire process makes the text more search-friendly and accessible. Also, at the same time, it helps in preserving the original structuring of the text, which can be repurposed or applied to create a new document for other purposes.
The OCR technology helps in automating the data extraction process from machine-printed or typed text into a scanned document or PDF file format and then translating them into the machine-encoded format for reading, searching, and editing purposes. It should be noted that the OCR Technology is highly dependent on the quality of the source paper copy and therefore scanned image.
Optical character recognition (OCR) is now used in different verticals where a large volume of paper documents accumulates such as
• The Insurance Sector
• Banking Sector
• Healthcare Sector
• Libraries
• Governmental Agencies, and so on.
Learn more: https://www.e-arc.com/blog/optical-character-recognition-ocr-technology/
In this file, a simple search for pattern recognition will be explained, and it contains a summary of everything you want to know, and there are some important sources and information in it.
download it from here: https://exe.io/56WbRkf
Developing a hands-free interface to operate a Computer using voice commandMohammad Liton Hossain
The main focus of this study is to help a handicap person to operate a computer by voice command. It can be used to operate the entire computer functions on the user’s voice commands. It makes use of the Speech Recognition technology that allows the computer system to identify and recognize words spoken by a human using a microphone. This Software will be able to recognize spoken words and enable user to interact with the computer. This interaction includes user giving commands to his computer which will then respond by performing several tasks, actions or operations depending on the commands they gave. For Example: Opening /closing a file in computer, YouTube automation using voice command, Google search using voice command, make a note using voice command, calculation by calculator using voice command etc.
COMP-111 Past Paper 2021 complete Solution PU BS 4 Year Programhaiderali8455
in this presentation we provide complete solution of past paper 2021 having course code comp-111 all the colleges of affliated with punjab university.
COMP-111 Past Paper 2021 Solution PU BS 4 Year Program |
Text Detection and Recognition with Speech Output for Visually Challenged Per...IJERA Editor
Reading text from scene, images and text boards is an exigent task for visually challenged persons. This task has been proposed to be carried out with the help of image processing. Since a long period of time, image processing has helped a lot in the field of object recognition and still an emerging area of research. The proposed system reads the text encountered in images and text boards with the aim to provide support to the visually challenged persons. Text detection and recognition in natural scene can give valuable information for many applications. In this work, an approach has been attempted to extract and recognize text from scene images and convert that recognized text into speech. This task can definitely be an empowering force in a visually challenged person's life and can be supportive in relieving them of their frustration of not being able to read whatever they want, thus enhancing the quality of their lives.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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1. 1
1.INTRODUCTION
In the running world, there is growing demand for the software systems to recognize characters in
computer world when information is scanned through paper documents. As we know that we have a
number of newspapers and books which are in printed format related to different subjects. In these days
there is a huge demand in “storing the information available in these paper documents in to a computer
storage disk and then later reusing this information by searching process, to avoid damages or losses”.
One simple way to store information in these paper documents in to computer system is to first scan the
documents and then store them as IMAGES. But to reuse this information it is very difficult to read the
individual contents and searching the contents form these documents line-by-line and word-by-word. The
reason for this difficulty is the font characteristics of the characters in paper documents are different to
font of the characters in computer system. As a result, computer is unable to recognize the characters
while reading them. This concept of storing the contents of paper documents in computer storage place
and then reading and searching the content is called DOCUMENT PROCESSING. Sometimes in this
document processing we need to process the information that is related to languages other than the
English in the world. For this document processing we need an application software system named
CHARACTER RECOGNITION SYSTEM. This process is also called CHARACTER RECOGNITION
AND CONVERSION (CRC).
Thus our need is to develop character recognition software system to perform Document Image
Analysis which transforms documents in paper format to electronic format. For this process there are
various techniques in the world. We’ve chosen Character Recognition and Conversion.
HISTORY OF OCR
Early optical character recognition may be traced to technologies involving 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. 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.
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.
With the advent of smart-phones and smart-glasses, 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-in to the operating system will typically use an
OCR API to extract the text from the image file captured and provided by the device. 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.
2. 2
TYPES OF OCR
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".
1.1 PURPOSE
The main purpose of Character Recognition and Conversion is to perform document
processing of electronic document formats converted from paper formats more effectively and
efficiently. This improves the accuracy of recognizing the characters during document
processing compared to various existing available character recognition methods. Here Character
Recognition technique derives the meaning of the characters, their font properties from their bit-
mapped images.
The primary objective is to speed up the process of character recognition in document
processing. As a result the system can process huge number of documents with-in less time
and hence saves the time.
It aims to recognize multiple heterogeneous characters that belong to different universal
languages with different font properties and alignments.
3. 3
1.2PROJECT SCOPE
The scope of our project Character Recognition and Conversion on a grid infrastructure is to
provide an efficient and enhanced software tool for the users to perform Document Image
Analysis, document processing by reading and recognizing the characters in research, academic,
governmental and business organizations that are having large pool of documented, scanned
images. Irrespective of the size of documents and the type of characters in documents, the
product is recognizing them, searching them and processing them faster according to the needs
of the environment.
1.3Technology Used
The exact mechanisms that allow humans to recognize objects are yet to be understood,
but the three basic principles are already well known by scientists – integrity, purposefulness and
adaptability (IPA). These principles allows to replicate natural or human-like recognition.
Let’s take a look on how OCR recognizes text. First, the program analyses the structure
of document image. It divides the page into elements such as blocks of texts, tables, images, etc.
The lines are divided into words and then - into characters. Once the characters have been
singled out, the program compares them with a set of pattern images. It advances numerous
hypotheses about what this character is. Basing on these hypotheses the program analyses
different variants of breaking of lines into words and words into characters. After processing
huge number of such probabilistic hypotheses, the program finally takes the decision, presenting
you the recognized text.
1.4How to use?
Using OCR is easy: the process generally consists of three stages: Open (Scan) the
document, Recognize it and then Save in a convenient format (DOC, RTF, XLS, PDF, HTML,
TXT etc.) or export data directly to one of Office applications such as Microsoft Word, Excel or
Adobe Acrobat.
4. 4
2.FEASIBILITY STUDY
A feasibility study is a high-level capsule version of the entire System analysis and Design
Process. The study begins by classifying the problem definition. Feasibility is to determine if it’s worth
doing. Once an acceptance problem definition has been generated, the analyst develops a logical model of
the system. A search for alternatives is analyzed carefully. There are 3 parts in feasibility study.
2.1 TECHNICAL FEASIBILITY
Evaluating the technical feasibility is the trickiest part of a feasibility study. This is because, at
this point in time, not too many detailed design of the system, making it difficult to access issues like
performance, costs on (on account of the kind of technology to be deployed) etc. A number of issues have
to be considered while doing a technical analysis. Understand the different technologies involved in the
proposed system before commencing the project we have to be very clear about what are the technologies
that are to be required for the development of the new system. Find out whether the organization currently
possesses the required technologies. Is the required technology available with the organization?
2.2 OPERATIONAL FEASIBILITY
Proposed project is beneficial only if it can be turned into information systems that will meet
the organizations operating requirements. Simply stated, this test of feasibility asks if the system will
work when it is developed and installed. Are there major barriers to Implementation? Here are
questions that will help test the operational feasibility of a project:
Is there sufficient support for the project from management from users? If the current system is well
liked and used to the extent that persons will not be able to see reasons for change, there may be
resistance.
Are the current business methods acceptable to the user? If they are not, Users may welcome a
change that will bring about a more operational and useful systems.
Have the user been involved in the planning and development of the project?
Early involvement reduces the chances of resistance to the system and in general and increases the
likelihood of successful project.
Since the proposed system was to help reduce the hardships encountered. In the existing manual
system, the new system was considered to be operational feasible.
5. 5
2.3 ECONOMIC FEASIBILITY
Economic feasibility attempts to weigh the costs of developing and implementing a new system,
against the benefits that would accrue from having the new system in place. This feasibility study gives
the top management the economic justification for the new system. A simple economic analysis which
gives the actual comparison of costs and benefits are much more meaningful in this case. In addition, this
proves to be a useful point of reference to compare actual costs as the project progresses. There could be
various types of intangible benefits on account of automation. These could include increased customer
satisfaction, improvement in product quality better decision making timeliness of information, expediting
activities, improved accuracy of operations, better documentation and record keeping, faster retrieval of
information, better employee morale.
6. 6
3.SOFTWARE USED
The software used in Character Recognition and Conversion (CRC) is MATLAB.
MATLAB (Matrix Laboratory) is a high-level language and interactive environment that enables
to you to perform computationally intensive faster than with traditional programming languages such as
C, C++ and Fortran.
MATLAB consists of 4 components such as;
Workspace
Command Window
Command History
File Editor Window
Workspace displays all the defined variables.
Command Window is used to execute commands in the MATLAB environment.
Command History displays record of the commands used.
File Editor Window defines your function.
3.1 Operating System
Windows 7 and higher versions
3.2 Hardware Requirement Specification
Processor: Intel core 2 dual or higher
RAM: 2GB
Memory Required: 5GB or higher
7. 7
4.SOFTWARE DESIGN
4.1 DATA FLOW DIAGRAM
The DFD is also called as bubble chart. A data-flow diagram (DFD) is a graphical
representation of the "flow" of data through an information system. DFD’s can also be used for the
visualization of data processing.
Figure 4.1: Level 0 DFD
Figure 4.2: Level 1 DFD
9. 9
5.CODING
5.1. Code for Creating Template
%Letter
clc;
close all;
A=imread('letters_numbersA.bmp');B=imread('letters_numbersB.bmp');
C=imread('letters_numbersC.bmp');D=imread('letters_numbersD.bmp');
E=imread('letters_numbersE.bmp');F=imread('letters_numbersF.bmp');
G=imread('letters_numbersG.bmp');H=imread('letters_numbersH.bmp');
I=imread('letters_numbersI.bmp');J=imread('letters_numbersJ.bmp');
K=imread('letters_numbersK.bmp');L=imread('letters_numbersL.bmp');
M=imread('letters_numbersM.bmp');N=imread('letters_numbersN.bmp');
O=imread('letters_numbersO.bmp');P=imread('letters_numbersP.bmp');
Q=imread('letters_numbersQ.bmp');R=imread('letters_numbersR.bmp');
S=imread('letters_numbersS.bmp');T=imread('letters_numbersT.bmp');
U=imread('letters_numbersU.bmp');V=imread('letters_numbersV.bmp');
W=imread('letters_numbersW.bmp');X=imread('letters_numbersX.bmp');
Y=imread('letters_numbersY.bmp');Z=imread('letters_numbersZ.bmp');
%lower case letters
a=imread('letters_numbersa.png');b=imread('letters_numbersb.png');
c=imread('letters_numbersc.png');d=imread('letters_numbersd.png');
e=imread('letters_numberse.png');f=imread('letters_numbersf.png');
g=imread('letters_numbersg.png');h=imread('letters_numbersh.png');
i=imread('letters_numbersi.png');j=imread('letters_numbersj.png');
k=imread('letters_numbersk.png');l=imread('letters_numbersl.png');
m=imread('letters_numbersm.png');n=imread('letters_numbersn.png');
o=imread('letters_numberso.png');p=imread('letters_numbersp.png');
q=imread('letters_numbersq.png');r=imread('letters_numbersr.png');
s=imread('letters_numberss.png');t=imread('letters_numberst.png');
u=imread('letters_numbersu.png');v=imread('letters_numbersv.png');
w=imread('letters_numbersw.png');x=imread('letters_numbersx.png');
y=imread('letters_numbersy.png');z=imread('letters_numbersz.png');
%Number
one=imread('letters_numbers1.bmp'); two=imread('letters_numbers2.bmp');
three=imread('letters_numbers3.bmp');four=imread('letters_numbers4.bmp');
five=imread('letters_numbers5.bmp'); six=imread('letters_numbers6.bmp');
seven=imread('letters_numbers7.bmp');eight=imread('letters_numbers8.bmp');
nine=imread('letters_numbers9.bmp'); zero=imread('letters_numbers0.bmp');
%*-*-*-*-*-*-*-*-*-*-*-
letter=[A B C D E F G H I J K L M...
N O P Q R S T U V W X Y Z];
number=[one two three four five...
six seven eight nine zero];
lowercase = [a b c d e f g h i j k ...
l m n o p q r s t u v w x y z];
character=[letter number lowercase];
templates=mat2cell(character,42,[24 24 24 24 24 24 24 ...
10. 10
24 24 24 24 24 24 24 ...
24 24 24 24 24 24 24 ...
24 24 24 24 24 24 24 ...
24 24 24 24 24 24 24 24 ...
24 24 24 24 24 24 24 24 ...
24 24 24 24 24 24 24 24 ...
24 24 24 24 24 24 24 24 ...
24 24]);
save ('templates','templates')
clear all
5.2. Read Lines
%function lines%
%
function [fl re]=lines_crop(im_texto)
% Divide text in lines
% im_texto->input image; fl->first line; re->remain line
% Example:
%clc;
%im_texto=imread('blackwhite.jpg');
%figure,imshow(im_texto)
%title('after bwareaopen')
%im_texto = bwareaopen(im_texto,60);
%figure,imshow(im_texto);
%title('after bwareaopen')
%[fl re]=lines(im_texto);
%subplot(3,1,1);imshow(im_texto);title('INPUT IMAGE')
%subplot(3,1,2);imshow(fl);title('FIRST LINE')
%subplot(3,1,3);imshow(re);title('REMAIN LINES')
im_texto=clip(im_texto);
num_filas=size(im_texto,1);
for s=1:num_filas
if sum(im_texto(s,:))==0
nm=im_texto(1:s-1, :); % First line matrix
%pause(1);
rm=im_texto(s:end, :);% Remain line matrix
%pause(1);
fl = clip(nm);
pause(1);
re=clip(rm);
%*-*-*Uncomment lines below to see the result*-*-*-*-
%subplot(2,1,1);imshow(fl);
%subplot(2,1,2);imshow(re);
break
else
fl=im_texto;%Only one line.
re=[ ];
end
end
%subplot(3,1,1);imshow(im_texto);title('INPUT IMAGE')
%subplot(3,1,2);imshow(fl);title('FIRST LINE')
%subplot(3,1,3);imshow(re);title('REMAIN LINES')
function img_out=clip(img_in)
11. 11
[f c]=find(img_in);
img_out=img_in(min(f):max(f),min(c):max(c));
5.3. Read Letters in a Line
%function lines%
%function letter_in_a_line
function [fl re space]=letter_crop(im_texto)
% Divide letters in lines
im_texto=clip(im_texto);
num_filas=size(im_texto,2);
%figure,imshow(im_texto);
%title('line sent in the function letter');
for s=1:num_filas
s;
sum_col = sum(im_texto(:,s));
if sum_col==0
k = 'true';
nm=im_texto(:,1:s-1); % First letter matrix
%figure,imshow(nm);
%title('first letter in the function letter_in_a_line');
%pause(1);
rm=im_texto(:,s:end);% Remaining line matrix
%figure,imshow(rm);
%title('remaining letters in the function letter_in_a_line');
%pause(1);
fl = clip(nm);
%pause(1);
re=clip(rm);
space = size(rm,2)-size(re,2);
%*-*-*Uncomment lines below to see the result*-*-*-*-
%subplot(2,1,1);imshow(fl);
%subplot(2,1,2);imshow(re);
break
else
fl=im_texto;%Only one line.
re=[ ];
space = 0;
end
end
function img_out=clip(img_in)
[f c]=find(img_in);
img_out=img_in(min(f):max(f),min(c):max(c));
12. 12
5.4. Recognize letters
%function read_letter
function letter=read_letter(imagn,num_letras)
% Computes the correlation between template and input image
% and its output is a string containing the letter.
% Size of 'imagn' must be 42 x 24 pixels
% Example:
% imagn=imread('D.bmp');
% letter=read_letter(imagn)
%load templates
global templates
comp=[ ];
for n=1:num_letras
sem=corr2(templates{1,n},imagn);
comp=[comp sem];
%pause(1)
end
vd=find(comp==max(comp));
%*-*-*-*-*-*-*-*-*-*-*-*-*-
if vd==1
letter='A';
elseif vd==2
letter='B';
elseif vd==3
letter='C';
elseif vd==4
letter='D';
elseif vd==5
letter='E';
elseif vd==6
letter='F';
elseif vd==7
letter='G';
elseif vd==8
letter='H';
elseif vd==9
letter='I';
elseif vd==10
letter='J';
elseif vd==11
letter='K';
elseif vd==12
letter='L';
elseif vd==13
letter='M';
elseif vd==14
letter='N';
elseif vd==15
letter='O';
elseif vd==16
14. 14
elseif vd==44
letter='h';
elseif vd==45
letter='i';
elseif vd==46
letter='j';
elseif vd==47
letter='k';
elseif vd==48
letter='l';
elseif vd==49
letter='m';
elseif vd==50
letter='n';
elseif vd==51
letter='o';
elseif vd==52
letter='p';
elseif vd==53
letter='q';
elseif vd==54
letter='r';
elseif vd==55
letter='s';
elseif vd==56
letter='t';
elseif vd==57
letter='u';
elseif vd==58
letter='v';
elseif vd==59
letter='w';
elseif vd==60
letter='x';
elseif vd==61
letter='y';
elseif vd==62
letter='z';
else
letter='l';
%*-*-*-*-*
end
5.5. Convert recognized letters to text format
% PRINCIPAL PROGRAM
warning off %#ok<WNOFF>
% Clear all
clc, close all, clear all
% Read image
imagen=imread('testcheck1.jpg');
% Show image
imagen1 = imagen;
15. 15
figure,imshow(imagen1);
title('INPUT IMAGE WITH NOISE')
% Convert to gray scale
if size(imagen,3)==3 %RGB image
imagen=rgb2gray(imagen);
end
% Convert to BW
threshold = graythresh(imagen);
imagen =~im2bw(imagen,threshold);
imagen2 = imagen;
%figure,imshow(imagen2);
% title('before bwareaopen')
% Remove all object containing fewer than 15 pixels
imagen = bwareaopen(imagen,15);
imagen3 = imagen;
%figure,imshow(imagen3);
%title('after bwareaopen')
%Storage matrix word from image
word=[ ];
re=imagen;
%Opens text.txt as file for write
fid = fopen('text.txt', 'wt');
% Load templates
load templates
global templates
% Compute the number of letters in template file
num_letras=size(templates,2);
while 1
%Fcn 'lines_crop' separate lines in text
[fl re]=lines_crop(re); %fl= first line, re= remaining image
imgn=fl;
n=0;
%Uncomment line below to see lines one by one
%figure,imshow(fl);pause(2)
%-----------------------------------------------------------------
spacevector = []; % to compute the total spaces betweeen
% adjacent letter
rc = fl;
while 1
%Fcn 'letter_crop' separate letters in a line
[fc rc space]=letter_crop(rc); %fc = first letter in the line
%rc = remaining cropped line
%space = space between the letter
% cropped and the next letter
%uncomment below line to see letters one by one
%figure,imshow(fc);pause(0.5)
img_r = imresize(fc,[42 24]); %resize letter so that correlation
%can be performed
n = n + 1;
spacevector(n)=space;
%Fcn 'read_letter' correlates the cropped letter with the images
%given in the folder 'letters_numbers'
letter = read_letter(img_r,num_letras);
16. 16
%letter concatenation
word = [word letter];
if isempty(rc) %breaks loop when there are no more characters
break;
end
end
%-------------------------------------------------------------------
%
max_space = max(spacevector);
no_spaces = 0;
for x= 1:n %loop to introduce space at requisite locations
if spacevector(x+no_spaces)> (0.75 * max_space)
no_spaces = no_spaces + 1;
for m = x:n
word(n+x-m+no_spaces)=word(n+x-m+no_spaces-1);
end
word(x+no_spaces) = ' ';
spacevector = [0 spacevector];
end
end
%fprintf(fid,'%sn',lower(word));%Write 'word' in text file (lower)
fprintf(fid,'%sn',word);%Write 'word' in text file (upper)
% Clear 'word' variable
word=[ ];
%*When the sentences finish, breaks the loop
if isempty(re) %See variable 're' in Fcn 'lines'
break
end
end
fclose(fid);
%Open 'text.txt' file
winopen('text.txt')
clear all
17. 17
6.SAMPLE TESTING
During the process of execution, it selects a file from a directory using a dialog box.
Figure 6.1: Select an Image Dialog box
The image which has been selected will be displayed in the processing platform.
Figure 6.2: Show Image with error
18. 18
The processed image will be displayed as a text in notepad.
Figure 6.3: Text generation in a textbox
19. 19
7.PERFORMANCE TESTING
There are many types of tastings which can be performed in the software. One of the testing is
black box testing.
7.1 Black Box Testing
In this type of testing the product design to perform is already known. The only thing to check
here is the input and its corresponding output. Out of millions of inputs we have consider only
four inputs.
Figure 7.1: Conversion of text from a single line image to text format
Figure 7.2: Conversion of text from a multi-line image to text format
20. 20
Figure 7.3: Conversion of text from a colored background image to text format
Figure 7.4: Conversion of text from a lower case letter format
Figure 7.5: Conversion of text from a lower case letter and upper case letter format
22. 22
8.ARCHITECTURE
The Architecture of the Character Recognition and Conversion system on a grid infrastructure
consists of the three main components. They are:-
Scanner
Character recognition Software
Output Interface
Figure 8.1: CRC Architecture
23. 23
9.APPLICATION
Language Conversion
Along with English, there are varieties of languages which can be converted into user readable
format. By language translation software which uses DIA as its main functioning software.
Automatic Number Plate Recognition
Automatic number plate recognition is a mass surveillance method that uses optical
character recognition on images to read vehicle registration plates. They can use existing closed-
circuit television or road-rule enforcement cameras, or ones specifically designed for the task.
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.
Data Entry for Business Documents
It is widely used as a form of data 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.
More applications,
More quickly make textual versions of printed documents
Make electronic images of printed documents searchable
Automatic insurance documents key information extraction
Converting handwriting in real time to control a computer
Assistive technology for blind and visually impaired users
24. 24
10. CONCLUSION
It is a powerful tool to recognize characters into electronic formats. As mentions earlier it can be used for
many purposes. However, the limited availability of capital could restrict the growth of this technology.
But if given proper enhancement it can be used for a variety of other purposes like, retinal scan,
recognition of new font and characters and can be used for storing large documents in electronic format
like one’s certificates and belongings.
25. 25
11. References
1. http://in.mathworks.com/help/vision/examples/recognize-text-using-optical-character-
recognition-ocr.html (6th Jan 2016, 19.14)
2. https://en.wikipedia.org/wiki/Optical_character_recognition(21st May 2016, 21.38)
3. http://www.advancedsourcecode.com/characterrecognition.asp(22nd may 2016, 22.13)
4. http://in.mathworks.com/help/vision/ref/ocr.html(21st May 2016, 20.46)
5. https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&cad=rja&uact=
8&sqi=2&ved=0ahUKEwjx34-
Op5nKAhWPGI4KHSX3ALgQtwIILjAD&url=https%3A%2F%2Fwww.youtube.com%2
Fwatch%3Fv%3D8Qjzkk-
h0p4&usg=AFQjCNEnGN163uiWbcciPic8ORNN6RHBRQ&bvm=bv.110151844,d.c2E(20t
h May 2016, 12.34)
6. Software Engineering, 9th
Edition, IAN SOMMERVILLE
7. http://in.mathworks.com/help/matlab/predefined-dialog-boxes.html(20th May 2016, 15.45)
8. in.mathworks.com/help/matlab/ref/uigetpref.html(20th May 2016, 16.10)
9. http://in.mathworks.com/help/matlab/predefined-dialog-boxes.html(20th May 2016, 16.15)
10. http://in.mathworks.com/videos/creating-a-gui-with-guide-68979.html(21st May 2016, 8. 25)
11. http://in.mathworks.com/help/search.html?qdoc=use+a+selected+image+taken+by+uigetfile
&submitsearch= (21st
May 2016, 8.45)
12. http://www.mathworks.com/matlabcentral/fileexchange/view_license?file_info_id=18169(21
st May 2016, 9.00)
13. http://www.mathworks.com/matlabcentral/fileexchange/18169-optical-character-
recognition--ocr-(21st May 2016, 10.30)
14. http://www.mathworks.com/matlabcentral/fileexchange/31322-optical-character-
recognition-lower-case-and-space-included-(21st May 2016, 11.00)
15. https://sourceforge.net/directory/os:windows/?q=character%20recognition%20using%20m
atlab(21st May 2016, 11.30)
16. http://www.caam.rice.edu/~timwar/CAAM210/OCR.html(21st May 2016, 12.00)
17. http://in.mathworks.com/help/vision/ref/ocr.html(21st May 2016, 12.15)
18. http://in.mathworks.com/help/vision/examples/recognize-text-using-optical-character-
recognition-ocr.html(22nd May 2016, 12.30)
19. http://www.springer.com/us/book/9781848003293(22nd May 2016, 16.00)
20. http://in.mathworks.com/help/images/(22nd May 2016, 16.15)