The document discusses the development of Grade Analyser, a web application that uses machine learning and natural language processing to automate the grading of subjective answer-type exams. It aims to address the inefficiencies and inaccuracies of manual grading by teachers. The application allows teachers to upload assignments and answer keys, and students to submit handwritten answer scripts digitally. Google Cloud Vision API converts handwriting to text for analysis using a Cosine similarity algorithm to determine similarity to the answers and grade responses fairly. The tool is intended to speed up grading, reduce errors, and ensure more impartial student assessments.

1. INTRODUCTION
The introduction of online classes and exams has had a significant impact on how we
measure and evaluate student performance in today's rapidly changing educational
environment. This change, though time-consuming and prone to error, has also highlighted the
shortcomings of conventional manual grading techniques. We present Grade Analyser, a
ground-breaking tool created to simplify the evaluation of handwritten answer scripts in the
digital age, in order to address these issues and improve the speed and accuracy of the grading
process.
Grade Analyser provides a complete solution for both educators and students, bridging
the divide between the traditional and digital spheres of education. Teachers can easily create
assignments and upload answer keywords using this application, and students can submit their
handwritten answer scripts in a digital format. The main features of Grade Analyser rely on
cutting-edge technology, such as Google Cloud Vision API, to convert handwritten scripts into
digital text so that automated evaluation can be performed on them.
The sophisticated grading algorithm of Grade Analyser determines the degree of
similarity between student responses and the provided answer keywords by using the Cosine
similarity algorithm in addition to extracting appropriate keywords. This innovative method
makes sure that each attempted response is fairly and accurately evaluated, and it ultimately
gives students' efforts the grades they deserve.
We want to revolutionalize grading with Grade Analyser by reducing the time restraints
and potential errors that come with manual evaluation. This application not only accelerates
the announcement of results but also ensures a more impartial assessment of student
performance, in line with the changing demands of contemporary education. We will learn how
Grade Analyser uses cutting-edge technology as we delve deeper into the specifics of this
project that brings in a new era of effective and precise grading for educational institutions.

2. PROBLEM STATEMENT
The goal of this project is to use NLP and Machine learning to assess subjective answer-type
tests. This tool may be utilized for verifying subjective response type tests at a variety of
educational institutions.
SCOPE AND IMPORTANCE
Manual grading of subjective answer-type examinations is a time-consuming and labor-
intensive process. It entails teachers physically handling and evaluating large numbers of
answer papers, which might take a long time. This might create delays in the announcement of
results, causing dissatisfaction and anxiety for both students and instructors.
Furthermore, hand grading is prone to inconsistencies and inaccuracies. Different teachers may
have different criteria and techniques for evaluating answers, resulting in grading differences.
Teachers may also make mistakes while transcribing grades or computing final scores in some
situations, which can exacerbate the problem.
The use of machine learning techniques to automate the grading process has the potential to
address these issues. The program can properly extract responses from images and determine
the key-points and expected points presented by the teacher by utilizing handwriting
recognition and NLP. The use of weightages provided by the teacher can also aid in the
consistency and accuracy of marks across multiple answer sheets.
Grading automation not only improves efficiency and accuracy, but it also reduces instructors'
responsibilities, allowing them to focus on curriculum development and student engagement.
Additionally, it provides pupils with immediate feedback for self-improvement.
In conclusion, employing natural language processing techniques to automate the grading of
subjective answer-type tests has the potential to transform the education industry by boosting
efficiency, eliminating mistakes, and offering more consistent and accurate grading.

3. Methodology
1. Project Initiation
1.1 Project Scope Definition
• Define the scope of the project, including key features, functionalities, and
deliverables.
• Identify the target users, primarily teachers and students.
1.2 Stakeholder Identification
• Identify all project stakeholders, including teachers, students, and potential
administrators.
1.3 Project Objectives
• Establish clear project objectives, including:
• Creation of a teacher interface for assignment creation and answer key upload.
• Development of a student interface for uploading handwritten answer scripts.
• Integration of Google Cloud Vision API for handwriting-to-text conversion.
• Implementation of a Cosine similarity algorithm for grading.
2. Requirements Analysis
2.1 Gather Requirements
• Conduct interviews and surveys with teachers and students to gather detailed
requirements for the application.
• Document functional and non-functional requirements.
2.2 Use Case Analysis
• Create use case diagrams and scenarios to understand how users will interact with the
system.
2.3 Data Model Design
• Design the database schema to store user information, assignments, answer keys, and
student submissions.

4. 3. System Design
3.1 Architecture Design
• Choose the appropriate architecture for the web application (e.g., client-server).
• Define the technology stack (programming languages, frameworks, databases, etc.).
3.2 User Interface Design
• Create wireframes and prototypes for both the teacher and student interfaces.
• Ensure user-friendly and intuitive design.
3.3 API Integration
• Integrate Google Cloud Vision API for handwriting recognition and transformation to
digital text.
3.4 Algorithm Design
• Design and implement the Cosine similarity algorithm for evaluating student answers
based on answer keys.
4. Development
4.1 Frontend Development
• Develop the frontend of the web application for both teachers and students.
• Implement features like assignment creation, answer key upload, and answer script
submission.
4.2 Backend Development
• Develop the server-side components, including API endpoints for data retrieval and
processing.
• Implement the database to store user data, assignments, and submissions.
5. Testing
5.1 Unit Testing
• Conduct unit testing for individual components and functions.

5. 5.2 Integration Testing
• Test the integration of frontend and backend components.
5.3 User Acceptance Testing (UAT)
• Invite teachers and students to participate in UAT to ensure the system meets their
requirements.
6. Deployment
6.1 Deployment Plan
• Create a deployment plan for hosting the web application on a server or cloud
platform.
6.2 User Training
• Provide training and documentation for teachers and students on how to use the
system.
7. Monitoring and Maintenance
7.1 Monitoring
• Implement monitoring tools to track application performance and user activity.
7.2 Bug Tracking
• Establish a system for reporting and tracking bugs and issues.
7.3 Continuous Improvement
• Gather feedback from users and make improvements based on their suggestions.
8. Project Closure
8.1 Documentation
• Create comprehensive documentation for the project, including user manuals and
technical documentation.

6. 8.2 Handover
• Hand over the project to the appropriate administrators or maintainers.
8.3 Evaluation
• Conduct a post-project evaluation to assess whether the project objectives were met
and identify lessons learned.
By following this methodology, you can systematically plan, develop, and deploy the grade
evaluator web application while ensuring that it meets the needs of teachers and students.

7. Feasibility Study for the Grade Analyser Web Application
1. Project Description and Objectives
• Provide a brief overview of the project, including its objectives and intended
outcomes.
2. Technical Feasibility
• Technology Stack: Assess the feasibility of using the chosen technologies for the
project. Consider factors like compatibility, scalability, and the availability of skilled
developers.
• API Integration: Evaluate the feasibility of integrating the Google Cloud Vision API
for handwriting recognition and text conversion.
3. Operational Feasibility
• User Needs: Analyze whether the web application meets the needs of teachers and
students effectively.
• User Acceptance: Determine if the target users (teachers and students) are willing to
adopt and use the application.
4. Economic Feasibility
• Cost Estimation: Calculate the estimated costs associated with development,
deployment, maintenance, and licensing (if any).
• Benefit Analysis: Estimate the potential benefits, such as time savings and improved
grading accuracy, and compare them to the costs to determine the economic
feasibility.
5. Schedule Feasibility
• Project Timeline: Create a detailed project timeline, including development, testing,
and deployment phases, to assess whether the project can be completed within the
desired timeframe.
6. Legal and Compliance Feasibility
• Data Privacy: Ensure that the project complies with data privacy regulations and
standards, especially when handling student data.

8. • Copyright and Licensing: Verify that there are no legal issues related to using
external APIs and libraries.
7. Risks and Mitigation
• Identify potential risks that could impact the project's success and outline strategies to
mitigate these risks.
8. Alternative Solutions
• Explore alternative solutions or approaches to achieve the project's objectives.
Compare these alternatives in terms of feasibility, cost, and benefits.
9. Recommendation
• Based on the analysis of technical, operational, economic, schedule, legal, and other
factors, provide a clear recommendation on whether to proceed with the project.

9. SOFTWARE REQUIREMENT SPECIFICATION
Data preprocessing
A component of data preparation, describes any type of processing performed on raw data to
prepare it for another data processing procedure.
Feature extraction
Feature extraction refers to the process of transformation of raw data into numerical features
that can be processed while preserving the information in the original data. This increases the
accuracy of the learning model by extraction features from input data.
Google cloud vision
Cloud Vision allows developers to easily integrate vision detection features within
applications, including image labeling, face and landmark detection, optical character
recognition (OCR), and tagging of explicit content.
Cosine similarity algorithm
Cosine similarity is used as a metric in different machine learning algorithms like the KNN for
determining the distance between the neighbors, in recommendation systems, it is used to
recommend movies with the same similarities and for textual data, it is used to find the
similarity of texts in the document.
Operating system: Windows 10/11
Front-end software: ReactJS
Back-end software: Flask, Firebase

10. Cost Estimation :
Evaluation of costs for this project is yet to be done as it is still in the initial stages.
Although the estimation is that cost requirement is low as all resources necessary are
freely available and data necessary will be provided from a trusted source.

11. References
[1]. "Machine Learning and Intelligent Communications: First International Conference,
MLICOM 2022" edited by Srikanta Patnaik, Swagatam Das, and Valentina Emilia Balas.
[2]. "Artificial Intelligence in Education: 19th International Conference, AIED 2018" edited
by Cristina Conati, Neil Heffernan, Antonija Mitrovic, and M. Felisa Verdejo.
[3]. Ganga Sanuval, Sayeeda Sameena Fathima “A Study of Automated Evaluation of
Student's Examination using Machine Language Techniques” 2021.
[4] Vijay Rowtula, Subba Reddy Oota, C.V. Jawahar “Towards Automated Evaluation
of Handwritten Assessments”.