Ijraset Journal For Research in Applied Science and Engineering Technology

Abstract

Introduction

I. INTRODUCTION

Character Recognition (CR) has been an active area of research in the past and due to its diverse applications it continues to be a challenging research topic. In this paper, we focus especially on offline recognition of handwritten English words by first detecting individual characters. The main approaches for offline handwritten word recognition can be divided into two classes, holistic and segmentation based. The holistic approach is used in recognition of limited size vocabulary where global features extracted from the entire word image are considered. As the size of the vocabulary increases, the complexity of holistic based algorithms also increases and correspondingly the recognition rate decreases rapidly. The segmentation based strategies, on the other hand, employ bottom-up approaches, starting from the stroke or the character level and going towards producing a meaningful word. After segmentation the problem gets reduced to the recognition of simple isolated characters or strokes and hence the system can be employed for unlimited vocabulary. We here adopt segmentation based handwritten word recognition where neural networks are used to identify individual characters. A number of techniques are available for feature extraction and training of CR systems in the literature, each with its own superiorities and weaknesses. We explore these techniques to design an optimal offline handwritten English word recognition system based on character recognition. Post processing technique that uses lexicon is employed to improve the overall recognition accuracy.

Handwriting  has  continued  as  a  means  of  communication in  our  day-to-day  life.  As  each  person's  handwriting  is unique,  it  is  sometimes  hard  to  interpret  the  information they  try  to  convey.  Handwriting  Recognition  is  an  ability  of a  computer  to  receive  and  interpret  intelligible handwritten  input  from  sources  such  as  paper  documents, photographs,  touch-screens  and  other  devices.  Though  it is  a  difficult  problem  due  to  the  great  variations  of  writing styles,  different  size  and  orientation  angle  of  the characters,  it  is  still  found  useful  for  the  applications  in some  way.  The  main  objective  is  to  understand  the handwriting  and  convert  it  into  readable  text,  which includes  characters,  words,  lines,  paragraphs  etc.  In  this project,  the  challenge  is  classifying  the  image  of  any handwritten  word,  which  might  be  of  the  form  of  cursive or  block  writing.  Along  with  this,  Text-to-Speech  is  used  to help  people  who  have  trouble  reading  on-screen  text.

A. Project Description

1. Purpose: The main purpose of our project is to build a system that is capable of recognizing handwritten characters from a user image input.
2. Objectives: The objective of this project is to recognize the handwritten characters of the user with the help of neural networks. We have to build suitable neural network and aim it properly. The program should be able to take out the characters one by one and plan the target output for training purpose. After automatic processing of the image, the training dataset has to be used to train “classification engine” for identification purpose.

II. LITERATURE SURVEY

Every Software development requires the survey process. The Survey process is needed to get the requirement for the software. The Survey also consists of studying the present system and also studying about the tools needed for the development of the software. A proper understanding of the tools is very much essential. Following is an extract of the information of the material collected during literature survey.

Hao  Zeng  et  al.  proposes  a  method  that  focuses  on  using  a simpler  neural  network  instead  of  complicated  ones  that require  high  quality  computer  configuration  to  recognize handwritten  digits  with  relatively  promising  accuracy[1].

MNIST  dataset  is  used  to  train  the  neural  network.  An Efficient  Algorithm  for  Real-Time  Handwritten  Character Recognition  in  Mobile  Devices[2]  that  is  used  to  measure the  algorithm  efficiency,  tests  were  applied  to  8  persons, who  are  related  to  the  computing  scene  and  outside  it, with  different  writing  styles.

Mobile  Client-Server Approach  for  handwritten  digit  recognition  [3]  is  another one,  in  which  CNN  was  also  used  to  improve  the performance  of  neural  networks.  The  digit  recognition consists  of  some  modules  for  the  processing.

Rohan  Vaidya  et  al.  designed  an  image  segmentation  based handwritten  character  recognition  system  using  DeepLearning  [4].  OpenCV  was  used  for  performing  Image processing  and  Tensor  flow  was  used  to  train  a  neural network. 

Haishi  Du  et  al.  proposed  a  system  that  identifies words  using  acoustic  signals  generated  by  pens  and  paper using  Deep-Learning  [5].  The  framework  is  created  with three  major  components:  segmentation,  classification,  and word  suggestion. 

Handwritten  Document  into  Digitized Text  Using  Segmentation  Algorithm  [6].  The  main  aim  is  to help  in  preserving  history  by  making  information searchable,  easily,  and  reportable  without  the  need  for human labor.

Edgard  Chammas  et  al.  proposed  a  mobile  application  that is  built  on  a  distributed  architecture  that  allows  tourists  to obtain  additional  information  about  location  and  menu entries  in  the  Arabic  language  [7]. 

The  recognition  of printed  texts  is  done  using  optical  character  recognition.   SolveIt-  an  Application  for  Automated  Recognition  and Processing  of  Handwritten  Mathematical  Equations[8].

Here  a  convolutional  neural  network  (CNN)  is  used  to classify  symbols.  The  recognized  symbols  are  strung together  to  form  an  equation  that  can  be  parsed  by  the math  engine  (SymPy2).  A  framework  that  takes  the  image of  multiple  printed-papers  using  a  mobile  device's  camera is used  in  Optical  Character  Recognition  (OCR) Performance  in  Server-based  Mobile  Environment.  After the  first  image  is  captured,  the  image  is  then  directly  sent to  a  server.    Server  processes  the  image  using  the  OCR application  directly  and  sends  the  text  file  back  to  the mobile  device  [9]. 

Handwritten  Character  Recognition  to obtain  Editable  Text  [10]  is  a  system  proposed  by  Ms.  Jyoti A.  Katkar.  No  internet  connectivity  is  required  for character  recognition  in  the  system.  And  the  system  offers 90% accuracy.

A. Existing System

In most of the existing systems recognition accuracy is heavily dependent on the quality of the input document. In handwritten text adjacent characters tend to be touched or overlapped. Therefore it is essential to segment a given string correctly into its character components. In most of the existing segmentation algorithms, human writing is evaluated empirically to deduce rules. But there is no guarantee for the optimum results of these heuristic rules in all styles of writing. Moreover handwriting varies from person to person and even for the same person it varies depending on mood, speed etc. This requires incorporating artificial neural networks, hidden Markov models and statistical classifiers to extract segmentation rules based on numerical data.

Disadvantages:

1. High Complexity.
2. Difficult to analysis.
3. Time Consumption Is More

B. Proposed System

The user  can  upload  the  image   from  the  system’s  storage.  The uploaded  image  is  then  processed  in  a  neural  network model  (NN  model)  which  identifies  the  characters,  i.e.;  the digits,  alphabets  or  special  symbols.  After  identifying  these characters,  they  are  converted  into  text  (printed  text)  and this  processed  document  is sent  back  to  the  user  as  output.

Advantages

1. Less Complicated.
2. Easy to process
3. Accuracy is more

C. Methodology

1. Image Preprocessing: In Image Preprocessing, this  phase  is  divided  into  two  parts:  Image  Preprocessing and  Segmentation.  Image  Preprocessing  involves  different stages  of  operations  in  order  to  enhance  the  image  for further  processing.  Preprocessing  involves  resizing  of image,  gray-scaling  and  binarization.    The  input  images can  be  of  varying  size,  so  the  images  are  resized  to  a standard  size  before  feeding  the  input  to  the  model.  Gray scaling  is  the  process  of  converting  an  image  from  other color  shades  to  shades  of  gray.  Gray-scaled  images  are typically  composed  of  shades  of  gray,  varying  from  black, at  the  weakest  intensity  to  white,  at  the  strongest.  The values  of  intensity  range  from  0  to  255.  Binarization  is  the process  of  transforming  character  image  into  the  binary  (0 and  1)  form.  It  is  an  important  stage  to  be  performed  on gray  scale  images.  Segmentation  and  character  recognition would  be  much  easier  once  the  process  of  binarization  is carried  out  in  a  proper  manner.  
2. Image Segmentation: Image  segmentation  is  the  process  of  partitioning  an image  into  multiple  segments.  Image  classification  is  the process  of  predicting  a  specific  class,  or  label,  for something  that  is  defined  by  a  set  of  data  points.  A comparison  takes  place  between  the  input  and  the  stored values  (patterns)  to  find  the  appropriate  match  class  for the  input  images.  The  training  and  test  dataset  are reshaped  so  that  the  refined  dataset  can  be  given  to  the model.
3. Neural  Network  Modeling and Training: A  Convolutional  Neural  Network  (CNN)  is  used  to perform  recognition  tasks  and  classification.  The  following steps  are  performed  in  the  CNN  model:  1.  Input  image  is fed  into  the  CNN  layers,  2.  Image  processing,  3. Comparison  and  prediction  takes  place  4.  Output  is  the printed  text.  A  CNN  model  for  recognizing  the  handwritten characters  was  created  using  Keras  and  Tensorflow backend.  Keras  is  an  open-source  software  library  that provides  a  Python  interface  for  artificial  neural  networks and  also  acts  as  an  interface  for  Tensorflow  library.  
4. Character  Recognition: The  handwritten  character  image  is  converted  into  printed text  by  sending  the  image  to  the  model.  In  the  model,  each character  is  recognized  after  preprocessing  and  they  are converted  into  readable  text.

D. Feasibility Study

The feasibility study proposes one or more feasible conceptual solutions to the problem set of the project. The conceptual solutions give an idea of what the new system will  Look like. They indicate what inputs are needed by the system and what outputs will be produced. Three things to be done to established feasibility. First, it must be checked that the project is technically feasible. Second, operational feasibility must be established. For this, it is necessary to consult the system users to see if the proposed solution satisfies user objectives and can be fitted in to current system operation. Third, economic feasibility must be checked. The study must determine whether the project’s goal can be achieved within the resource limits allocated to it. It must also determine whether it is worthwhile to proceed with the project at all or whether the benefits obtained from the new system are not worth the cost, in which case the project will be terminated. Feasibility study is necessary to determine whether the proposed system is feasible considering the technical, operational and economic factors. By having detailed feasibility study one can have a clear view of the proposed system with respect to its benefits and draw backs.

For a successful feasibility study of system feasibility, the existing systems and proposed system are studied carefully.

System Feasibility

The feasibility study is carried out to determine whether the proposed system can be developed with the available resources.

• Technical Feasibility

• Economical Feasibility
• Behavioral Feasibility
• Motivational Feasibility
• Schedule Feasibility
• Operational Feasibility

1. Technical Feasibility: Technical feasibility is the study of resource availability that may affect the ability to achieve an acceptable system. Technical feasibility is the most difficult area to ensure at initial stages. Since the objectives functions and performance cannot be predicted to its fullest, everything seems possible provided proper assumptions are made. It is essential that the process of technical feasibility. The consideration that is normally associated with technical feasibility included resource availability at the organization where the project is to be developed and implemented.
2. Economical Feasibility: An evaluation of development cast weighted against the ultimate income or benefit derived from the developed system. Economical economic justification includes a broad range of concerns that include cost-benefit analysis. Cost benefit delineates costs for project development and weighs them against tangible and intangible benefits of a system. Regarding the cost and benefits, the project, which is to man-hours with compared to man that are required to record data about activity task report manually and also in terms of money benefits by the selling of this system as a product. Thus this project work is economically feasible for the development in any company.
3. Motivational Feasibility: An evaluation of the probability that the company is significantly motivated to support the development and implementation of the application with necessary user participation, resources, training etc. the participation and support by the organization during system study was encouraging thus eliminating any resistance in this regard. So from behavioral aspect the new system is supposed to have efficient from the company.
4. Schedule Feasibility: The time schedule required for the development of this project is very important since over-runs result in escalated projects costs and also hinders in the development of the other systems.
5. Operational Feasibility: The project is going to be used by the organization under different circumstances. Anyone can work with this application as it supports user-friendly approach. It provides graphical user interfaces to the user, so that user can easily interact with the system. Users no need to have the knowledge about ASP.Net, MSSQL concepts to use the application. The application is designed in such a way that it can be easily implemented in any android version device or cell.

E. Project Development Life Cycle

PDLC is considered to be the foundation for all software development methodologies, with various activities associated with each level. Activities such as budgets, requirements gathering, and documentation writing, are included in the cycle, as well as the more technical elements. PDLC usually begins with determining customer business needs, which is followed by implementation and testing. The cycle ends when all requirements have been fulfilled. The software development life cycle comprises of seven distinct phases:

1. Phase 1: Planning

In the planning phase, project goals are determined and a high-level plan for the intended project is established. Planning is the most fundamental and critical organizational phase. The three primary activities involved in the planning phase are as follows:

Identification of the system for development

Feasibility assessment

Creation of project plan

2. Phase 2: Analysis

In the analysis phase, end user business requirements are analyzed and project goals converted into the defined system functions that the organization intends to develop. The three primary activities involved in the analysis phase are as follows:

Gathering business requirement

Creating process diagrams

Performing a detailed analysis

Business requirement gathering is the most crucial part at this level of PDLC. Business requirements are a brief set of business functionalities that the system needs to meet in order to be successful. Technical details such as the types of technology used in the implementation of the system need not be defined in this phase. A sample business requirement might look like “The system must track all the employees by their respective department, region, and the designation”. This requirement is showing no such detail as to how the system is going to implement this requirement, but rather what the system must do with respect to the business.

3. Phase 3: Design

In the design phase, we describe the desired features and operations of the system. This phase includes business rules, pseudo-code, screen layouts, and other necessary documentation. The two primary activities involved in the design phase are as follows:

Designing of IT infrastructure

Designing of system model

To avoid any crash, malfunction, or lack of performance, the IT infrastructure should have solid foundations. In this phase, the specialist recommends the kinds of clients and servers needed on a cost and time basis, and technical feasibility of the system. Also, in this phase, the organization creates interfaces for user interaction. Other than that, data models and entity relationship diagrams (ERDs) are also created in the same phase.

4. Phase 4: Development

In the development phase, all the documents from the previous phase are transformed into the actual system. The two primary activities involved in the development phase are as follows:

Development of IT infrastructure

Development of database and code

In the design phase, only the blueprint of the IT infrastructure is provided, whereas in this phase the organization actually purchases and installs the respective software and hardware in order to support the IT infrastructure. Following this, the creation of the database and actual code can begin to complete the system on the basis of given specifications.

5. Phase 5: Testing

In the testing phase, all the pieces of code are integrated and deployed in the testing environment. Testers then follow Software Testing Life Cycle activities to check the system for errors, bugs, and defects to verify the system’s functionalities work as expected or not, often. The two primary activities involved in the testing phase are as follows:

Writing test cases

Execution of test cases

Testing is a critical part of software development life cycle. To provide quality software, an organization must perform testing in a systematic way. Once test cases are written, the tester executes them and compares the expected result with an actual result in order to verify the system and ensure it operates correctly. Writing test cases and executing them manually is an intensive task for any organization, which can result in the success of any business if executed properly.

6. Phase 6: Deployment

During this next phase, the system is deployed to a real-life (the client’s) environment where the actual user begins to operate the system. All data and components are then placed in the production environment. This phase is also called referred to as ‘delivery.’

7. Phase 7: Maintenance

In the maintenance phase, any necessary enhancements, corrections, and changes will be made to make sure the system continues to work, and stay updated to meet the business goals. It is necessary to maintain and upgrade the system from time to time so it can adapt to future needs. The three primary activities involved in the maintenance phase are as follows:

Support the system users

System maintenance

System changes and adjustment

F. Tools And Technologies Used

1. Overview Of Python

Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming. Python is often described as a "batteries included" language due to its comprehensive standard library. Python was conceived in the late 1980s as a successor to the ABC language. Python 2.0, released in 2000, introduced features like list comprehensions and a garbage collection system capable of collecting reference cycles. Python 3.0, released in 2008, was a major revision of the language that is not completely backward-compatible, and much Python 2 code does not run unmodified on Python 3. The Python 2 language, i.e. Python 2.7.x, was officially discontinued on January 1, 2020 (first planned for 2015) after which security patches and other improvements will not be released for it. With Python 2's end-of-life, only Python 3.5.x and later are supported.

Python interpreters are available for many operating systems. A global community of programmers develops and maintains CPython, an open source reference implementation. A non-profit organization, the Python Software Foundation, manages and directs resources for Python and CPython development.

Python was conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands as a successor to the ABC language (itself inspired by SETL),capable of exception handling and interfacing with the Amoeba operating system. Its implementation began in December 1989. Van Rossum shouldered sole responsibility for the project, as the lead developer, until July 12, 2018, when he announced his "permanent vacation" from his responsibilities as Python's Benevolent Dictator For Life, a title the Python community bestowed upon him to reflect his long-term commitment as the project's chief decision-maker. He now shares his leadership as a member of a five-person steering council. In January, 2019, active Python core developers elected Brett Cannon, Nick Coghlan, Barry Warsaw, Carol Willing and Van Rossum to a five-member "Steering Council" to lead the project. Python 2.0 was released on 16 October 2000 with many major new features, including a cycle-detecting garbage collector and support for Unicode.

Python 3.0 was released on 3 December 2008. It was a major revision of the language that is not completely backward-compatible. Many of its major features were backported to Python 2.6.x and 2.7.x version series. Releases of Python 3 include the 2to3 utility, which automates (at least partially) the translation of Python 2 code to Python 3. Python 2.7's end-of-life date was initially set at 2015 then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python 3.

2. OpenCV

OpenCV (Open source computer vision) is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez (which was later acquired by Intel). The library is cross-platform and free for use under the open-source BSD license.

Officially launched in 1999 the OpenCV project was initially an Intel Research initiative to advance CPU-intensive applications, part of a series of projects including real-time ray tracing and 3D display walls. The main contributors to the project included a number of optimization experts in Intel Russia, as well as Intel's Performance Library Team. In the early days of OpenCV, the goals of the project were described as:

Advance vision research by providing not only open but also optimized code for basic vision infrastructure. No more reinventing the wheel. Disseminate vision knowledge by providing a common infrastructure that developers could build on, so that code would be more readily readable and transferable.

Advance vision-based commercial applications by making portable, performance-optimized code available for free – with a license that did not require code to be open or free itself.

The second major release of the OpenCV was in October 2009. OpenCV 2 includes major changes to the C++ interface, aiming at easier, more type-safe patterns, new functions, and better implementations for existing ones in terms of performance (especially on multi-core systems). Official releases now occur every six months and development is now done by an independent Russian team supported by commercial corporations.

3. NUMPY

NumPy is a Python package which stands for ‘Numerical Python’. It is the core library for scientific computing, which contains a powerful n-dimensional array object; provide tools for integrating C, C++ etc. It is also useful in linear algebra, random number capability etc. NumPy array can also be used as an efficient multi-dimensional container for generic data. Now, let me tell you what exactly is a python numpy array.

NumPy is the fundamental package for scientific computing with Python. It contains among other things:

• A powerful N-dimensional array object
• Sophisticated (broadcasting) functions
• Tools for integrating C/C++ and Fortran code
• Useful linear algebra, Fourier transform, and random number capabilities

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

4. Panda

Pandas is an open-source Python Library providing high-performance data manipulation and analysis tool using its powerful data structures. The name Pandas is derived from the word Panel Data – an Econometrics from Multidimensional data.

In 2008, developer Wes McKinney started developing pandas when in need of high performance, flexible tool for analysis of data.

Prior to Pandas, Python was majorly used for data munging and preparation. It had very little contribution towards data analysis. Pandas solved this problem. Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the origin of data load, prepare, manipulate, model, and analyse

Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.

Key Features of Pandas

• Fast and efficient Data Frame object with default and customized indexing.
• Tools for loading data into in-memory data objects from different file formats.
• Data alignment and integrated handling of missing data.
• Reshaping and pivoting of date sets.
• Label-based slicing, indexing and subsetting of large data sets.
• Columns from a data structure can be deleted or inserted.
• Group by data for aggregation and transformations.
• High performance merging and joining of data.
• Time Series functionality.

III. SOFTWARE REQUIREMENT SPECIFICATION

Software Requirement Specification (SRS) is a fundamental document, which forms the foundation of the software development process. SRS not only lists the requirements of a system but also has a description of its major features. These recommendations extend the IEEE standards. The recommendations would form the basis for providing clear visibility of the product to be developed serving as baseline for execution of a contract between client and the developer.

A system requirement is one of the main steps involved in the development process. It follows after a resource analysis phase that is the task to determine what a particular software product does. The focus in this stage is one of the users of the system and not the system solutions. The result of the requirement specification document states the intention of the software, properties and constraints of the desired system.

SRS constitutes the agreement between clients and developers regarding the contents of the software product that is going to be developed. SRS should accurately and completely represent the system requirements as it makes a huge contribution to the overall project plan.

The software being developed may be a part of the overall larger system or may be a complete standalone system in its own right. If the software is a system component, the SRS should state the interfaces between the system and software portion.

Spiral model is one of the most important Software Development Life Cycle models, which provides support for Risk Handling. In its diagrammatic representation, it looks like a spiral with many loops. The exact number of loops of the spiral is unknown and can vary from project to project. Each loop of the spiral is called a Phase of the software development process. The exact number of phases needed to develop the product can be varied by the project manager depending upon the project risks. As the project manager dynamically determines the number of phases, so the project manager has an important role to develop a product using spiral model.

The Radius of the spiral at any point represents the expenses(cost) of the project so far, and the angular dimension represents the progress made so far in the current phase.

Each phase of Spiral Model is divided into four quadrants as shown in the above figure. The functions of these four quadrants are discussed below-

• Objectives determination and identify alternative solutions: Requirements are gathered from the customers and the objectives are identified, elaborated and analyzed at the start of every phase. Then alternative solutions possible for the phase are proposed in this quadrant.
• Identify and resolve Risks: During the second quadrant all the possible solutions are evaluated to select the best possible solution. Then the risks associated with that solution is identified and the risks are resolved using the best possible strategy. At the end of this quadrant, Prototype is built for the best possible solution.
• Develop next version of the Product: During the third quadrant, the identified features are developed and verified through testing. At the end of the third quadrant, the next version of the software is available.
• Review and plan for the next Phase: In the fourth quadrant, the Customers evaluate the so far developed version of the software. In the end, planning for the next phase is started.

1. Risk Handling in Spiral Model

A risk is any adverse situation that might affect the successful completion of a software project. The most important feature of the spiral model is handling these unknown risks after the project has started. Such risk resolutions are easier done by developing a prototype. The spiral model supports coping up with risks by providing the scope to build a prototype at every phase of the software development.

Prototyping Model also support risk handling, but the risks must be identified completely before the start of the development work of the project. But in real life project risk may occur after the development work starts, in that case, we cannot use Prototyping Model. In each phase of the Spiral Model, the features of the product dated and analyzed and the risks at that point of time are identified and are resolved through prototyping. Thus, this model is much more flexible compared to other PDLC models.

2. Why Spiral Model is called Meta Model?

The Spiral model is called as a Meta Model because it subsumes all the other SDLC models. For example, a single loop spiral actually represents the Iterative Waterfall Model. The spiral model incorporates the stepwise approach of the Classical Waterfall Model. The spiral model uses the approach of Prototyping Model by building a prototype at the start of each phase as a risk handling technique. Also, the spiral model can be considered as supporting the evolutionary model – the iterations along the spiral can be considered as evolutionary levels through which the complete system is built.

Advantages of Spiral Model: Below are some of the advantages of the Spiral Model.

• Risk Handling: The projects with many unknown risks that occur as the development proceeds, in that case, Spiral Model is the best development model to follow due to the risk analysis and risk handling at every phase.
• Good for large projects: It is recommended to use the Spiral Model in large and complex projects.
• Flexibility in Requirements: Change requests in the Requirements at later phase can be incorporated accurately by using this model.
• Customer Satisfaction: Customer can see the development of the product at the early phase of the software development and thus, they habituated with the system by using it before completion of the total product.

Disadvantages of Spiral Model: Below are some of the main disadvantages of the spiral model complex: 

• The Spiral Model is much more complex than other PDLC models.
• Expensive: Spiral Model is not suitable for small projects as it is expensive.
• Too much dependable on Risk Analysis: The successful completion of the project is very much dependent on Risk Analysis. Without very highly experienced expertise, it is going to be a failure to develop a project using this model.
• Difficulty in time management: As the number of phases is unknown at the start of the project, so time estimation is very difficult.

A. Users

Type of users:  End User

B. Functionl And Non-Functional Requirements

1. Functional Requirements

Functional requirements for a system describe the functionality or services that the system is expected to provide. Functional Requirements define the fundamental actions that must take place in application in accepting and in processing the inputs and generating the expected outputs. These depend on the type of system being developed and the expected users of the system and the user requirements from the system under development.

a. Data Load: The system provides a user with a graphical interface to Upload Data ,which is used to build Model.

b. Pre-processing: The image uploaded contains unwanted noise etc. they also have a big resolution. Aim of is to make the image suitable for feature extraction.

a. Image Cropping and Image Resolution: Adobe photoshop is used to crop the left and right margins of the image. The size of the image is also reduced, and then the image is converted to PNG format.

b. Removal of Noise

• Gaussian Noise: This is an electronic noise which is caused by thermal vibration of atoms and nature of warm objects.
• Gamma Noise: This is seen in laser based images.

c. Grayscale and Binarization: We created two-valued binary image and convert this image plane into foreground and background pixels can take either 0 or 255 (black or white), an inverted binary function can be created so that pixels are converted to either 0 or 255.

2. Handwriting Features

There are large numbers of features for handwriting of a person. Only seven significant features are extracted, which are popular.

a. Baseline: It is the imaginary line where the writer tends to write. The writer may write above, below or at the middle of the line. It brings out the emotional control of the writer.

b. Letter Size: It determines the size of the letter. It may be tall or short. Average height of word is considered. It tells wether the writer is introvert or extrovert.

c. Line Spacing: It the spacing between two consecutive lines. It gives clues of thinking clarity and the interaction with the environment

d. Word Spacing: The distance that is present between two words. This feature counts the pixels between two words It determines the emotional comfort of a person with other people.

e. Top Margin: It is simply the space left from the top of a page. If the top margin is narrow it shows lack of respect for others, if it is broad it shows modesty and formality.

f. Pen Pressure: It determines whether the user presses more on upstroke or downstroke (light or heavy).If it is heavy it determines strong willed and if it is light it determines sensitive, not strong willed.

g. Slant of Letters: It looks wether the writing is slant to the left or right. It can also be straight. The average slant is right. If left or right it determines goal oriented and sensitive. If it is straight the person might be independent and good decision maker.

3. Non Functional Requirements

a. Performance Requirements: The product should support the end users requirements. The product is capable of processing when the large numbers of files are provided as input and also it must be interactive and the delays involved should be less. So in every action-response of the system, there are no immediate delays.

b. Safety and Security Requirements: The system should be designed in a secured way by applying safety measures.  Information transmission should be securely transmitted to nodes without any changes in information. Special exception handling mechanism should be in place to avoid system errors.

c. Software Quality Attribute Availability: The application will not hang and opens quickly and with 99.9% uptime.

d. Reliability: The system should not crash and should identify invalid input and produce suitable error message.

e. Usability: The interface should be intuitive and easily navigable and user friendly.

f. Integrity: The software does not store any cache data or doesn’t use system resources in background.

C. Hardware And Software Requirements

1. Hardware Requirements

a. Processor                     :              Intel i5 2.53GHz

b. Hard Disk                    :              30GB

c. Ram                                             :              4 GB or above

2. Software Requirements

a. Operating system        :              Windows 8 and above

b. Coding Language       :              Python

c. Version                        :              3.7 & above

d. IDE                               :              IDLE

IV. System Design

A. Architecture

Three-tier (layer) is a client-server architecture in which the user interface, business process (business rules) and data storage and data access are developed and maintained as independent modules or most often on separate platforms.

The Architecture of Application is based on three-tier architecture. The three logical tiers are

1. Presentation tier - Tkinter
2. Middle tier – Python classes
3. Data tier

The main reason for considering three-tier architecture for the Application is as follows:

B. Flexibility

1.  Management of data is independent from the physical storage support,
2. Maintenance of the business logic is easier,
3.  Migration to new graphical environments is faster.
4.  If there is a minor change in the business logic, we don’t have to install the entire system in individual user’s PCs.

C. Reusability

Reusability of business logic is greater for the presentation layer. As this component is developed and tested, we can use it in any other project and would be helpful for future use.

D. Security

More secured architecture since the client cannot access the database directly.

E. Presentation Tier

The presentation tier for the application is python classes’ tkinter is used to take care of the user interaction and the visual part of the project. The various Web forms used by clients are listed below.

F. Middle Tier

The Middle Tier or Business Logic layer consists of python classes.

G. Data Tier

The Data Tier layer consists of database such as the data tier mainly concentrated on manipulating the data using a database management system. Here we implement the data tier using MS Access. Different varieties of data base are Oracle, SQL

V. DETAILED DESIGN

Detailed design starts after the system design phase is completed and the system design has been certified through the review. The goal of this phase is to develop the internal logic of each of the modules identified during system design.

In the system design, the focus is on identifying the modules, whereas during detailed design the focus is on designing the logic for the modules. In other words in system design attention is on what components are needed, while in detailed design how the components can be implemented in the software is the issue. The design activity is often divided into two separate phase system design and detailed design. System design is also called top-level design.  At the first level focus is on deciding which modules are needed for the system, the specifications of these modules and how the modules should be interconnected. This is called system design or top level design. In the second level the internal design of the modules or how the specifications of the module can be satisfied is decided. This design level is often called detailed design or logic design.

A. Data Flow Diagram

DFD graphically representing the functions, or processes, which capture, manipulate, store, and distribute data between a system and its environment and between components of a system. The visual representation makes it a good communication tool between User and System designer. Structure of DFD allows starting from a broad overview and expand it to a hierarchy of detailed diagrams. DFD has often been used due to the following reasons:

1. Logical information flow of the system
2. Determination of physical system construction requirements
3. Simplicity of notation
4. Establishment of manual and automated systems requirements

B. Use Case Diagram

Use case diagram is a graph of actors, a set of use cases enclosed by a system boundary, communication associations between the actor and the use case. The use case diagram describes how a system interacts with outside actors; each use case represents a piece of functionality that a system provides to its users. A use case is known as an ellipse containing the name of the use case and an actor is shown as a stick figure with the name of the actor below the figure. The use cases are used during the analysis phase of a project to identify and partition system functionality. They separate the system into actors and use case. Actors represent roles that are played by user of the system. Those users can be humans, other computers, pieces of hardware, or even other software systems.

C. Sequence Diagram

A sequence diagram shows object interactions arranged in time sequence. It depicts the objects and classes involved in the scenario and the sequence of messages exchanged between the objects needed to carry out the functionality of the scenario. Sequence diagrams are sometimes called event diagrams, event scenarios.

UML sequence diagrams are used to represent or model the flow of messages, events and actions between the objects or components of a system. Time is represented in the vertical direction showing the sequence of interactions of the header elements, which are displayed horizontally at the top of the diagram Sequence Diagrams are used primarily to design, document and validate the architecture, interfaces and logic of the system by describing the sequence of actions that need to be performed to complete a task or scenario. UML sequence diagrams are useful design tools because they provide a dynamic view of the system behavior.

1. Purpose: The sequence diagram is used primarily to show the interactions between objects in the sequential order that those interactions occur. One of the primary uses of sequence diagrams is in the transition from requirements expressed as use cases to the next and more formal level of refinement.

D. Activity Diagrams

Activity diagrams represent the business and operational workflows of a system. An Activity diagram is a dynamic diagram that shows the activity and the event that causes the object to be in the particular state. It is a simple and intuitive illustration of what happens in a workflow, what activities can be done in parallel, and whether there are alternative paths through the workflow.

VI.  IMPLEMENTATION

Implementation is the process of converting a new or a revised system design into an operational one. The objective is to put the new or revised system that has been tested into operation while holding costs, risks, and personal irritation to the minimum. A critical aspect of the implementation process is to ensure that there will be no disrupting the functioning of the organization. The best method for gaining control while implanting any new system would be to use well planned test for testing all new programs. Before production files are used to test live data, text files must be created on the old system, copied over to the new system, and used for the initial test of each program.

Another factor to be considered in the implementation phase is the acquisition of the hardware and software. Once the software is developed for the system and testing is carried out, it is then the process of making the newly designed system fully operational and consistent in performance.

 Implementation is the most crucial stage in achieving a successful system and giving the user’s confidence that the new system is workable and effective. Implementation of a modified application to replace an existing one. This type of conversation is relatively easy to handle, provide there are no major changes in the system. 

A. System Implementation

There are three major types of implementation are there but the following are proposed for the project.

B. Parallel Conversion type of Implementation

In this type of implementation both the current system and the proposed system run in parallel. This happens till the user gets the complete confidence on the proposed system and hence cuts of the current system.

C. Phase - In Method of Implementation

In this type of implementation the proposed system is introduced phase-by-phase. This reduces the risk of uncertainty of proposed system.

Each program is tested individually at the time of development using the data and has verified that this program linked together in the way specified in the programs specification, the computer system and its environment is tested to the satisfaction of the user. The system that has been developed is accepted and proved to be satisfactory for the user. And so the system is going to be implemented very soon. A simple operating procedure is included so that the user can understand the different functions clearly and quickly.

Initially as a first step the executable form of the application is to be created and loaded in the common server machine which is accessible to the entire user and the server is to be connected to a network. The final stage is to document the entire system which provides components and the operating procedures of the system. 

Implementation is the stage of the project when the theoretical design is turned out into a working system. Thus it can be considered to be the most critical stage in achieving a successful new system and in giving the user, confidence that the new system will work and be effective. The implementation stage involves careful planning, investigation of the existing system and it’s constraints on implementation, designing of methods to achieve changeover and evaluation of changeover methods.

Implementation is the process of converting a new system design into operation. It is the phase that focuses on user training, site preparation and file conversion for installing a candidate system. The important factor that should be considered here is that the conversion should not disrupt the functioning of the organization.

D. Implementation Methodology of the Project

The project is implemented in modular approach. Each module is coded as per the requirements and tested and this process is iterated till the all the modules have been thoroughly implemented.

E. Algorithms used in Project KNN

K-nearest neighbours (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems. However, it is mainly used for classification predictive problems in industry.

K-nearest neighbours (KNN) algorithm uses ‘feature similarity’ to predict the values of new data points which further means that the new data point will be assigned a value based on how closely it matches the points in the training set. We can understand its working with the help of following steps −

Step 1 − For implementing any algorithm, we need dataset. So during the first step of KNN, we must load the training data from the heart disease patients datalist  and  test data from the new patient entry info currently being taking tests.

Test data set – collected data samples.

Train data set – UCI & real time data from hospital.

Step 2 − Next, we need to choose the value of K i.e. the nearest data points. K can be any integer.

Here k is taken from test data set, which acts like a centroid point.

Step 3 − For each point in the test data do the following −

• 3.1 − Calculate the distance between test data and each row of training data with the help of any of the method namely: Euclidean, Manhattan or Hamming distance. The most commonly used method to calculate distance is Euclidean.
• 3.2 − Now, based on the distance value, sort the patients data based on parameters like age, gender, alcohol, bp, smoking parameters as preference of data in ascending order.
• 3.3 − Next, it will choose the top K rows from the sorted array.
• 3.4 − Now, it will assign a class to the test point based on most frequent segments from the patient data of these rows.

Step 4 – End

In our project we compare k value with train data set using KNN methodology likes Euclidean, Manhattan, where we cluster the values based on the nearest distance. Here we get nearest matches of heart disease preference to test data from train data set in turn we predict the content values from it.

F. Support Vector Machine (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However,  it is mostly used in classification problems. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well .

1. Identify the right hyper-plane (Scenario-1): Here, we have three hyper-planes (A, B and C). Now, identify the right hyper-plane to classify star and circle. You need to remember a thumb rule to identify the right hyper-plane: “Select the hyper-plane which segregates the two classes better”. In this scenario, hyper-plane “B” has excellently performed this job.
2. Identify the right hyper-plane (Scenario-2): Here, we have three hyper-planes (A, B and C) and all are segregating the classes well. Now, How can we identify the right hyper-plane?Here, maximizing the distances between nearest data point (either class) and hyper-plane will help us to decide the right hyper-plane. This distance is called as Margin. Let’s look at the below snapshot:

Above, you can see that the margin for hyper-plane C is high as compared to both A and B. Hence, we name the right hyper-plane as C. Another lightning reason for selecting the hyper-plane with higher margin is robustness. If we select a hyper-plane having low margin then there is high chance of miss-classification.

3. Identify the right hyper-plane (Scenario-3): Hint: Use the rules as discussed in previous section to identify the right hyper-plane Some of you may have selected the hyper-plane B as it has higher margin compared to A. But, here is the catch, SVM selects the hyper-plane which classifies the classes accurately prior to maximizing margin. Here, hyper-plane B has a classification error and A has classified all correctly. Therefore, the right hyper-plane is A.

4. Can we classify two classes (Scenario-4)?: Below, I am unable to segregate the two classes using a straight line, as one of the stars lies in the territory of other(circle) class as an outlier.  As I have already mentioned, one star at other end is like an outlier for star class. The SVM algorithm has a feature to ignore outliers and find the hyper-plane that has the maximum margin. Hence, we can say, SVM classification is robust to outliers.

5. Find the hyper-plane to segregate to classes (Scenario-5): In the scenario below, we can’t have linear hyper-plane between the two classes, so how does SVM classify these two classes? Till now, we have only looked at the linear hyper-plane.  SVM can solve this problem. Easily! It solves this problem by introducing additional feature. Here, we will add a new feature z=x^2+y^2. Now, let’s plot the data points on axis x and z:

In above plot, points to consider are:

• All values for z would be positive always because z is the squared sum of both x and
• In the original plot, red circles appear close to the origin of x and y axes, leading to lower value of z and star relatively away from the origin result to higher value of z.

G. NAÏVE BAYES

It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.

Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.

VII. SOFTWARE TESTING

Testing is the major process involved in software qualityassurance (QA). It is iterative process.  Here test data is prepared and is used to test the modules individually. System testing makes sure that all components of the system function properly as a unit by actually forcing the system to fail.

The test causes should be planned before testing begins. Then as the testing progresses, testing shifts focus in an attempt to find errors in integrated clusters of modules and in the entire system. The philosophy behind testing is to find errors. Actually testing is the estate of implementation that is aimed at ensuring that the system works actually and efficiently before implementation.

Testing is done for each module. After testing all the modules, the modules are integrated and testing of the final system is done with the test data, specially designed to show that the system will operate successfully in all its aspects conditions. The procedure level testing is made first. By giving improper inputs, the errors occurred are noted and eliminated. Thus the system testing is a confirmation that all is correct and an opportunity to show the user that the system works. The final step involves Validation testing, which determines whether the software function as the user expected. The end-user rather than the system developer conduct this test most software developers as a process called “Alpha and Beta test” to uncover that only the end user seems able to find.

This is the final step in system life cycle. Here we implement the tested error-free system into real-life environment and make necessary changes, which runs in an online fashion. Here system maintenance is done every months or year based on company policies, and is checked for errors like runtime errors, long run errors and other maintenances like table verification and reports.

During the requirement analysis and design, the output is a document that is usually textual and non-executable. After the coding phase, computer programs are available that can be executed for testing purpose. This implies that testing not only has to uncover errors introduced during coding, but also errors introduced during the previous phases.

The various types of testing done on the system are:

• Unit Testing
• Integration Testing
• Validation Testing
• System Testing
• Acceptance Testing

A. Unit Testing

Unit testing verification efforts on the smallest unit of software design, module. This is known as “Module Testing”. The modules are tested separately. This testing is carried out during programming stage itself. In these testing steps, each module is found to be working satisfactorily as regard to the expected output from the module.

B. Integration Testing

Integration testing is a systematic technique for constructing tests to uncover error associated within the interface. In the project, all the modules are combined and then the entire programmer is tested as a whole. In the integration-testing step, all the error uncovered is corrected for the next testing steps.   

C. Validation Testing

To uncover functional errors, that is, to check whether functional characteristics confirm to specification or not specified.

D. System Testing

Once individual module testing completed, modules are assembled to perform as a system. Then the top down testing, which begins from upper level to lower level module testing, has to be done to check whether the entire system is performing satisfactorily.

After unit and integration testing are over then the system as whole is tested. There are two general strategies for system testing.

They are:                                                                             

1. Code Testing: This strategy examines the logic of the program. A path is a specific combination of conditions handled by the program. Using this strategy, every path through the program is tested.
2. Specification Testing: This strategy examines the specifications stating what the program should do and how it should perform under various conditions. The test cases are developed for each condition of developed system and processed. It is found that the system developed perform according to its specified requirements. The system is used experimentally to ensure that the software will run according to tits specification and in the way user expect. Specification Testing is done successfully by entering various types of end data. It is checked for both valid and invalid data and found System is working properly as per requirement.

E. Acceptance Testing

When the system has no measure problem with its accuracy, the system passes through a final acceptance test. This test confirms that the system needs the original goal, Objective and requirements established during analysis. If the system fulfils all the requirements, it is finally acceptable and ready for operation.

F.  Plan

A software project test plan is a document that describes the objectives, scope approach and focus of a software testing effort. This process of preparing a test plan is a useful way to think through the efforts needed to validate the acceptability of a software product. The completed document will help the people outside the test group understand ‘Why and How’ of production validation. Different test plans are used at different levels of testing.

1. Test Plans used in Unit Testing: Each module is tested for correctness whether it is meeting all the expected results.Condition loops in the code are properly terminated so that they don’t enter into an infinite loop. Proper validations are done so as to avoid any errors related to data entry from user.

VIII. FUTURE ENHANCEMENTS

The future  work  can  be  to include more features from the micro approach of handwriting

analysis  like  the  loops  of  alphabet  'f'  and  'l',  gradient,  concavity  of  letters  and  so  on  in  order  to  predict  more accurate results.

Conclusion

This paper carries out a study of various feature based classification techniques for offline handwritten character recognition. After experimentation, it proposes an optimal character recognition technique. The proposed method involves segmentation of a handwritten word by using heuristics and artificial intelligence. Three combinations of Fourier descriptors are used in parallel as feature vectors. Support vector machine is used as the classifier. Post processing is carried out by employing lexicon to verify the validity of the predicted word. The results obtained by using the proposed Character Recognition system are found to be satisfactory.

References

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Copyright

Copyright © 2022 Saniya Firdous. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.