AI DRIVEN HANDWRITTEN PAPER EVALUATION

SYSTEM

A PROJECT REPORT

Submitted by

SAHANA V (Reg.No.61781921110044)
UMA MAGESHWARI M (Reg.No.61781921110058)

in partial fulfillment for the award of the degree

of
BACHELOR OF TECHNOLOGY
IN
ARTIFICIAL INTELLIGENCE AND DATA SCIENCE
SONA COLLEGE OF TECHNOLOGY

ANNA UNIVERSITY: CHENNAI 600 025

MAY 2024
 BONAFIDE CERTIFICATE
Certified that this project report “AI DRIVEN HANDWRITTEN PAPER EVALUATION

SYSTEM” is the bonafide work of SAHANA V (61781921110044) ,

UMAMAGESHWARI M (61781921110058), who carried out the project work under my

supervision.

SIGNATURE SIGNATURE

(Ms Sangeetha Priya R) Sangeethapriya R

Assistant Professor Assistant Professor
HEAD OF THE DEPARTMENT SUPERVISOR
Department of Information Technology Department of Information Technology
Sona College of Technology Sona College of Technology
Salem-636 005 Salem-636 005

Submitted for U19IT801 – Project Work viva voce examination held on

…………………

INTERNAL EXAMINER EXTERNAL EXAMINER

CHAPTER PAGE
TITLE
NO. NO.

LIST OF FIGURES v

LIST OF SYMBOLS AND ABBREVIATIONS vi

ABSTRACT vii

1 INTRODUCTION 1

1.1 About the Project 1

1.2 Scope for Future Development 2

2 LITERATURE SURVEY 3

HARDWARE AND SOFTWARE

3 4
REQUIREMENTS

4 PROJECT DESCRIPTION 5

4.1 Aim 5

4.2 System Architecture 6

4.2.1.1 Figure: Overall System Architecture 7

4.2.2 OCR-based Text Extraction 7

4.2.2.1 Figure: OCR-Based Text Extraction Flowchart 7

4.2.4 Evaluation and Scoring Engine 8

4.2.4.1 Figure: Answer Evaluation and Scoring Engine 8

4.3 AI and Machine Learning Models 9

CHAPTER PAGE
TITLE
NO. NO.

4.3.1 EasyOCR for Text Recognition 9

4.3.1.1 Figure: EasyOCR Text Recognition Output 10

4.3.2 TrOCR for Advanced Recognition 9

4.3.2.1 Figure: TrOCR Processing Pipeline 10

4.3.3 Similarity Matching with RapidFuzz 10

4.3.3.1 Figure: RapidFuzz Similarity Matching Workflow 10

Figure: Flask Web Interface – Upload & Result

4.4.2.1 12
Display

4.4.1 Customization Based on User Needs 12

4.4.2 Tailored Features for Diverse Educational Settings 12

4.5 Scope of the Project 13

5 RESULTS 16

5.1 Predictions 16

Figure: Sample Output with Student vs Answer Key

5.2.1 18
Comparison

5.2.2 Figure: PDF Evaluation Report Screenshot 20

5.2.3 Figure: CSV Exported Result Sheet Example 20

5.2 Outputs 20
CHAPTER PAGE
TITLE
NO. NO.

5.3 Source Code and Project Structure 21

6 CONCLUSION AND FUTURE WORK 44

6.1 Conclusion 44

6.2 Future Work 45

REFERENCES 46

CONFERENCE DETAILS 47
FIG NO TITLE PAGE NO

4.2.1.1 Overall System Architecture 7

4.2.2.1 OCR-Based Text Extraction Flowchart 7
4.2.4.1 Answer Evaluation and Scoring Engine 8
4.3.1.1 EasyOCR Text Recognition Output 10
4.3.2.1 TrOCR Processing Pipeline 10
4.3.3.1 RapidFuzz Similarity Matching Workflow 10
4.4.2.1 Flask Web Interface – Upload & Result Display 12
5.2.1 Sample Output with Student vs Answer Key Comparison 18
5.2.2 PDF Evaluation Report Screenshot 20
5.2.3 CSV Exported Result Sheet Example 20
 LIST OF SYMBOLS AND ABBREVIATIONS

OCR Optical Character Recognition

AI Artificial Intelligence

ML Machine Learning

TrOCR Transformer-based OCR model

EasyOCR Python-based OCR tool for text detection

TPR True Positive Rate

TP True Positives

FP False Positives

FN False Negatives

F1 Harmonic Mean of Precision and Recall

API Application Programming Interface

PDF Portable Document Format

CV2 OpenCV Library for Image Processing

HTML HyperText Markup Language

Flask Lightweight Python Web Framework

UI User Interface

GPU Graphics Processing Unit

JPEG / PNG Image Formats used for scanned exam papers

vi
 ABSTRACT

The integration of artificial intelligence (AI) and machine learning (ML) in the

"AI-Driven Handwritten Evaluation System" marks a transformative shift in

academic assessment and feedback automation. Leveraging cutting-edge

technologies such as optical character recognition (OCR), natural language

processing (NLP), and similarity analysis algorithms, this system reimagines

traditional exam evaluation by replacing manual checking with intelligent,

accurate, and scalable automation. Central to the innovation is the dual use of

EasyOCR and TrOCR, which enable robust recognition of handwritten text

from scanned exam papers, regardless of handwriting style and image quality.

This system empowers educators with the ability to upload both student exam

responses and standard answer keys, allowing the platform to extract, analyze,

and evaluate content in real-time. Using the RapidFuzz library for semantic

similarity comparison, it accurately computes marks based on the relevance and

closeness of student answers to the key, ensuring fairness and consistency

across assessments. The evaluation engine is built on a Flask-based web

interface, ensuring accessibility, simplicity, and transparency, while integration

with MySQL allows secure storage and retrieval of student data and results.

In an era where digital transformation is redefining educational methodologies,

the "AI-Driven Handwritten Evaluation System" addresses a critical gap in

academic institutions—minimizing evaluation time, reducing human error, and

increasing efficiency. Designed for scalability, this system is suitable for

schools, universities, and large-scale examination bodies. It enables data-driven

insights, visual result generation, and performance tracking, thus facilitating a

modern feedback loop between students and educators.

Ultimately, this project represents a paradigm shift in education technology. It

not only streamlines evaluation procedures but also paves the way for intelligent

assessment tools that support personalized learning, academic growth, and

digital academic governance. The "AI-Driven Handwritten Evaluation System"

is a pioneering step toward the future of smart education.

viii
 ACKNOWLEDGEMENT

First and foremost, we thank to power of almighty for showing us inner peace and
for all blessings. Special gratitude to our parents, for showing their support and love always.

We like to acknowledge the constant support provided by Sri.C.Valliappa, Chairman,

for his consistent motivation in pursuing my project.

Our gratitude thanks to our Vice Chairmen Sri. Chocko Valliappa and Sri. Thyagu
Valliappa who leads us in a narrow path towards success in all the way.

We are immensely grateful to our principal Dr.S.R.R.Senthilumar who has been our
constant source of inspiration.

We express our sincere thanks to the Head of Information Technology,

Dr.J.Akilandeswari,M.E,Ph.D, for providing adequate laboratory facilities to complete this
thesis.

We feel elated to keep on record our heartfelt thanks and gratitude to our project
guide Ms Sangeetha Priya R Professor/IT our steadfast inspiration, for his valuable
guidance, untiring patience and diligent encouragement during the entire span of this project.

We extend our heartfelt gratitude to Dr Suresh Y., Associate Professor/IT, for his
invaluable mentorship throughout this project journey. His insightful guidance, unwavering
support, and profound expertise have been instrumental in shaping our endeavors.

We feel proud in sharing this success with my staff members, non-teaching staffs and
friends who helped directly or indirectly in completing this project successfully.

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 CHAPTER 1
INTRODUCTION
"AI-Driven Handwritten Paper Evaluation System" introduces a
revolutionary approach to academic assessment by integrating Artificial
Intelligence (AI), Machine Learning (ML), and Optical Character Recognition
(OCR) technologies. In an educational landscape that demands efficiency,
accuracy, and scalability, this project redefines how handwritten exam papers
are evaluated by automating the traditionally manual process with intelligent
systems.

The core objective of the system is to enhance the accuracy, speed, and
consistency of evaluating student answer scripts. It allows educators to upload
scanned handwritten exam sheets along with standard answer keys, after which
it automatically extracts, processes, and evaluates the responses using advanced
NLP and similarity analysis techniques. The system is built using a powerful
combination of EasyOCR and TrOCR for text recognition, RapidFuzz for
similarity comparison, and a Flask-based web application for intuitive
interaction and result presentation.

By pushing the frontier of educational technology, the "AI-Driven

Handwritten Evaluation System" stands as a benchmark in digital assessment
solutions. It not only increases operational efficiency but also aligns with the
broader vision of digital transformation in education, ensuring a smarter, faster,
and more transparent future for academic evaluation systems.

1.1 ABOUT THE PROJECT

The AI-Driven Handwritten Evaluation System is a comprehensive solution

designed to automate the process of evaluating handwritten exam papers using
advanced Artificial Intelligence and Machine Learning techniques. The project
aims to reduce the time, effort, and inconsistencies often associated with manual
paper correction by enabling intelligent scanning, extraction, and evaluation of
written content.

At the heart of this system lies the integration of Optical Character Recognition
(OCR) technologies such as EasyOCR and Microsoft’s TrOCR, which facilitate
the accurate conversion of handwritten text into machine-readable format. Once
1
the text is extracted from both the student’s answer script and the standard
answer key, it is analyzed using natural language processing and text similarity
algorithms—particularly the RapidFuzz library—to determine content
alignment and score allocation.

Designed with scalability and adaptability in mind, the project can be extended
to support multiple subjects, varied answer formats, and integration with
existing academic management systems. The AI-Driven Handwritten
Evaluation System is not just a tool for automation—it is a transformative
educational aid that ensures fairness, consistency, and efficiency in academic
evaluations.

1.2 SCOPE FOR FUTURE DEVELOPMENT

The current system serves as a foundational model that can be
extended and improved in multiple dimensions:
 Multi-language Support: Integration of support for regional and
international languages for broader application.
 Diagram and Formula Recognition: Incorporating deep learning-
based recognition for diagrams, graphs, and mathematical
equations.
 Student Analytics Dashboard: Providing detailed performance
trends and subject-wise analytics over time.
 Voice-based Feedback: Implementing AI-generated verbal
feedback for enhanced learning experiences.
 Integration with LMS: Connecting with Learning Management
Systems (LMS) for seamless data flow between evaluation and
grading platforms.
 Mobile App Interface: Developing Android/iOS applications for
on-the-go exam evaluation and report access.

2
 CHAPTER 2
LITERATURE SURVEY
2.1. Integration of OCR in AI-Based Evaluation Systems
This study investigates the application of Optical Character Recognition (OCR)
technologies like EasyOCR and Microsoft's TrOCR in automating the
evaluation of handwritten academic responses. It highlights how OCR enables
the accurate extraction of textual data from scanned answer scripts, significantly
reducing manual work and improving evaluation consistency in educational
systems.
2.2. Role of Machine Learning in Handwritten Answer Assessment
This paper examines the use of machine learning algorithms in comparing
handwritten student responses with predefined answer keys. It emphasizes the
utilization of string similarity measures such as RapidFuzz and natural language
processing (NLP) models to assess the semantic and syntactic correctness of
student responses, thereby automating subjective answer evaluation.
2.3. Enhancing Accuracy with Fuzzy Matching Techniques
This research explores the effectiveness of fuzzy string matching algorithms
like Levenshtein Distance, RapidFuzz, and Jaccard Similarity in evaluating
student answers. These techniques allow for minor variations in wording and
spelling, making the automated system tolerant to common student errors while
still maintaining evaluation fairness.
2.4. Flask-Based Web Applications for AI Integration
The study presents how lightweight web frameworks such as Flask can be
employed to create robust frontends for AI-driven systems. It discusses the
benefits of integrating OCR models, image upload capabilities, and real-time
results visualization using Python Flask, offering educators an accessible and
interactive interface for automated assessments.
2.5. Future Directions in AI-Powered Educational Tools
This paper outlines the future advancements in AI-driven education systems,
including voice-based feedback generation, adaptive learning models, multi-
language OCR support, and integration with institutional Learning Management
Systems (LMS
3
 CHAPTER 3
HARDWARE AND SOFTWARE REQUIREMENTS
HARDWARE REQUIREMENTS
 Processor: Intel Core i5 or higher (64-bit architecture)
 RAM: Minimum 8 GB (16 GB recommended for model training)
 Hard Disk: 500 GB SSD (to support faster data read/write operations)
 GPU: Minimum 4 GB dedicated graphics card (NVIDIA recommended
for deep learning tasks)
 Scanner / Camera: High-resolution document scanner or camera for
capturing handwritten exam scripts
 Internet Connectivity: Required for model updates, cloud integration, and
remote access
SOFTWARE REQUIREMENTS
 Operating System: Windows 10/11, macOS, or Linux (Ubuntu
recommended for ML environments)
 Programming Language: Python 3.8+
 Frameworks & Libraries:
o EasyOCR / TrOCR (for handwritten text recognition)
o RapidFuzz / NLTK (for similarity matching and evaluation)
o Flask (for web application interface)
o OpenCV (for image preprocessing and manipulation)
o scikit-learn / TensorFlow / PyTorch (for ML model development)
 Database: MySQL (for storing questions, answers, results, and user data)
 Code Editor / IDE: Visual Studio Code (VS Code

4
 CHAPTER 4
PROJECT DESCRIPTION

The AI-Driven Handwritten Evaluation System is a transformative initiative

aimed at automating and enhancing the evaluation of handwritten exam scripts
using advanced technologies such as Optical Character Recognition (OCR),
Artificial Intelligence (AI), and Machine Learning (ML). This system is
designed to scan, interpret, and assess student responses by comparing them
with pre-defined answer keys, enabling accurate, unbiased, and rapid evaluation.
The system is deployed via a user-friendly web interface built with Flask,
supporting efficient exam management and result dissemination. By integrating
OCR models such as EasyOCR and Microsoft's TrOCR with intelligent
similarity matching algorithms like RapidFuzz, the project aspires to bring a
revolution in academic assessments, reducing human effort and increasing
evaluation transparency.
This system not only improves evaluation efficiency but also offers educational
institutions the ability to analyze performance metrics, generate instant reports,
and reduce manual overhead. The holistic design promotes scalability, allowing
for integration with diverse educational formats and question structures. With a
strong focus on fairness, accuracy, and adaptability, this project contributes to
the modernization of academic assessment in digital learning environments.

4.1 AIM
The aim of the AI-Driven Handwritten Evaluation System project is to develop
an intelligent and automated framework that can evaluate handwritten student
answer sheets by leveraging OCR, Artificial Intelligence (AI), and Machine
Learning (ML) techniques. The system is intended to:
 Scan and extract handwritten text from uploaded exam scripts.
 Compare student answers with predefined answer keys using similarity
matching.
 Allocate marks based on the degree of similarity and key concept
matching.
 Generate performance reports instantly through an interactive web
application.

5
The ultimate goal is to enhance evaluation accuracy, ensure transparency, and
reduce the workload on educators, thereby contributing to the digital
transformation of academic assessments.

FIG 4.1 SUGGESTED SYSTEM

4.2 INTELLIGENT EVALUATION SYSTEM

The AI-Driven Handwritten Evaluation System marks a pioneering

advancement in educational technology, aiming to automate and enhance the
evaluation process for handwritten answer scripts. This section delves into the
seamless integration of AI, OCR, and machine learning technologies, crafted to
optimize the accuracy, efficiency, and reliability of academic assessments for
students and educators.

4.2.1 INTEGRATION OF OCR MODELS

6
At the heart of the system lies the integration of powerful Optical Character
Recognition (OCR) models, including EasyOCR and Microsoft’s TrOCR.
These models are specifically chosen for their high accuracy in recognizing
handwritten text across varied writing styles. By using a dual-model approach,
the system ensures that the text extracted from scanned exam papers is both
precise and reliable, which serves as the foundational input for the evaluation
process. This combination allows seamless text detection across different scripts
and page qualities.

4.2.2 REAL-TIME TEXT EXTRACTION AND COMPARISON

A core feature of the evaluation system is its ability to extract handwritten

answers in real-time and compare them against a pre-uploaded answer key.
This is achieved using text similarity algorithms like RapidFuzz, which
measure how closely the student’s answer aligns with the expected response.
The model dynamically allocates marks based on keyword detection, semantic
similarity, and contextual relevance, offering a more human-like and intelligent
evaluation experience

7
 4.2.2.1 OCR-Based Text Extraction Flowchart

4.2.3 ACCURATE AND ADAPTIVE MARKING LOGIC

The system employs adaptive logic that adjusts the evaluation based on question
complexity and weightage. Educators can define custom marking schemes,
giving them control over the marking granularity. Furthermore, as the dataset
grows, the system continues to learn and refine its scoring capabilities, enabling
better alignment with real-world teacher assessments over time.

4.2.4.1 Answer Evaluation and Scoring Engine

4.2.4 PERFORMANCE ANALYTICS AND REPORT GENERATION

Beyond evaluation, the system incorporates interactive analytics dashboards
that provide students and teachers with real-time performance metrics. Key
features include:
 Per-question analysis
 Time taken per answer (optional via timestamps)
8
  Strength/weakness mapping
 Class-level insights
Reports can be generated and exported in PDF/CSV formats, offering
transparency and easy distribution.

4.2.5 USER-FRIENDLY WEB INTERFACE

Designed with accessibility in mind, the system is deployed through a Flask-
based web application that ensures an intuitive and responsive user experience.
Educators can:
 Upload question papers and answer keys
 View and verify student submissions
 Review results with visual feedback Students, on the other hand, can
access their results, download reports, and even see keyword-level
insights into their performance.

In summary, the AI-Driven Handwritten Evaluation System represents a

state-of-the-art solution to modern academic challenges. With its seamless OCR
integration, intelligent evaluation algorithms, adaptive marking logic, insightful
analytics, and accessible web interface, the system redefines the landscape of
academic assessments—enabling speed, fairness, and data-driven learning for
students and institutions alike.
4.3 ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING
(ML) INTEGRATION
Artificial Intelligence (AI) and Machine Learning (ML) form the core
technological pillars of the AI-Driven Handwritten Evaluation System,
enabling intelligent automation of the evaluation process. This section outlines
the key AI and ML components integrated into the system to achieve high
accuracy in answer recognition, text analysis, and automated scoring.

4.3.1 UTILIZATION OF COMPUTER VISION ALGORITHMS

9
The system employs advanced computer vision algorithms to analyze scanned
images of handwritten answer sheets. These algorithms process the input image,
detect text regions, and prepare the image for OCR (Optical Character
Recognition). Preprocessing techniques such as image binarization, noise
removal, and contour detection enhance the clarity of handwritten text and
isolate relevant answer regions. By incorporating computer vision pipelines, the
system ensures that even low-quality or poorly scanned documents are
accurately interpreted, paving the way for reliable text extraction and evaluation.

4.3.1.1 EasyOCR Text Recognition Output 4.3.2.1 TrOCR Processing Pipeline 4.3.3.1 RapidFuzz Similarity

Matching Workflow

4.3.2 MACHINE LEARNING MODELS FOR INTELLIGENT SCORING

At the heart of the evaluation engine lies a machine learning-based scoring
module that analyzes extracted answers and evaluates their similarity with the
predefined answer key. Using RapidFuzz, a powerful fuzzy string matching
library, the system computes semantic similarity scores between student
answers and ideal responses. These scores are then mapped to a dynamic
grading scale based on keyword presence, sentence structure, and contextual
relevance. Over time, the system can adapt to different question types and
grading styles, offering a personalized and scalable evaluation process for
various academic levels and institutions.

10
4.3.3 CONVOLUTIONAL NEURAL NETWORK (CNN) FOR
HANDWRITING DETECTION
A Convolutional Neural Network (CNN) is employed to support handwritten
character classification and pattern recognition. The CNN model is trained on
large datasets of handwritten characters to recognize and differentiate between
letters, numbers, and symbols in varying styles. During training, the CNN learns
hierarchical features—such as edges, curves, and strokes—that characterize
handwriting patterns. Once trained, the CNN is capable of generalizing across
diverse handwriting samples and improving the robustness of OCR-based text
extraction.
4.3.4 YOLO v8 FOR REGION DETECTION
The system integrates YOLO v8 (You Only Look Once), a real-time object
detection algorithm, to identify and extract answer blocks and question
numbers from the scanned answer sheet. YOLO v8 divides the input image
into a grid and detects bounding boxes around relevant content areas—such as
handwritten answers, questions, or marks—along with confidence scores. This
ensures structured extraction, where each answer is isolated and mapped to
the corresponding question, enabling accurate comparison and evaluation.
During the training phase, YOLO v8 is trained on annotated datasets of answer
sheets containing labeled regions of interest. Once integrated, the model
operates on every uploaded image to segment answer areas, reject irrelevant
noise, and facilitate structured data flow into the OCR and evaluation pipelines.
In conclusion, the integration of Artificial Intelligence and Machine Learning in
the AI-Driven Handwritten Evaluation System significantly enhances the
accuracy, adaptability, and scalability of academic assessments. Through the
combination of CNN for handwritten character recognition, YOLO v8 for
region detection, computer vision .

4.4 INCLUSIVE DESIGN FOR EDUCATIONAL INSTITUTIONS

Inclusivity is a core value of the AI-Driven Handwritten Evaluation System,
focusing on making the evaluation process accessible to a wide range of
educators and students regardless of their technical background, handwriting
style, or physical abilities. The project recognizes the diverse needs within
11
educational environments and strives to create an interface and evaluation
pipeline that is user-friendly, adaptable, and supportive of equity in education.

4.4.1 CUSTOMIZATION BASED ON USER NEEDS

A significant aspect of the system is the incorporation of customizable options
to address the varying capabilities and requirements of users. For example,
some educators may prefer simplified workflows with minimal technical
interactions, while others may require advanced analytics and export tools.
The system allows:
 Interface scaling for visual comfort,
 Upload methods for users with motor limitations (drag and drop,
clickable input, mobile upload),
 Integration of screen readers and keyboard navigation for visually
impaired users.
This customizable structure ensures that the system remains inclusive, enabling
educators from varied institutions — including rural or resource-constrained
ones — to participate in digital assessment without needing technical expertise.

4.4.2.1 Flask Web Interface – Upload & Result Display

12
4.4.2 TAILORED FEATURES FOR DIVERSE EDUCATIONAL
SETTINGS
To accommodate a broad range of use cases, the evaluation system provides
specific tailored features such as:
 Flexible Answer Input Formats: Accepts multiple formats including
scanned handwritten papers, smartphone-captured images, and PDFs.
 Multilingual Support (Upcoming): Future versions will support
regional language OCR for local curriculum compatibility.
 Feedback Integration: Personalized feedback generation using NLP is
planned for students needing performance improvement.
 Accessibility Enhancements: Incorporation of visual and audio cues,
progress loaders, and clear error messaging to help less-experienced users
navigate the interface.
These features are designed to ensure that all users — teachers, students, and
admins — can interact with the system effectively, regardless of ability level,
resource access, or infrastructure limitations.
4.5 SCOPE OF THE PROJECT
The scope of the AI-Driven Handwritten Evaluation System involves the
design, development, testing, and deployment of a fully automated handwritten
exam assessment platform. The system is intended for use in schools, colleges,
and competitive exam boards to modernize traditional evaluation systems using
AI technologies.

1. OCR and Handwriting Recognition System

 Integration of OCR tools such as EasyOCR and TrOCR for accurate text
extraction from scanned handwritten answer scripts.
 Preprocessing techniques including thresholding, resizing, and noise
removal to improve OCR accuracy.

2. AI and Machine Learning-Based Evaluation

13
  Implementation of fuzzy string similarity algorithms (e.g., RapidFuzz) for
intelligent comparison of student answers with answer keys.
 Use of adjustable scoring logic to account for partial correctness and
keyword-based marking.

3. Inclusive UI Design for Teachers and Students

 Development of a user-friendly web interface using Flask for uploading,
viewing, and downloading evaluations.
 Design of simplified workflows and intuitive forms for non-technical
users.
 Support for both English and regional languages in the future version.

4. Web and Database Integration

 MySQL integration for secure storage of answers, results, user metadata,
and feedback records.
 Ability to filter, sort, and export student results in PDF or CSV format
from the database.

5. Advanced Result Features and Export

 CSV and PDF generation modules to allow teachers to easily archive or
share student results.
 Live result visualization on-screen with performance breakdown per
student/question.

6. Training, Deployment, and Testing

 Creating detailed documentation and user guides for easy system
adoption.
 Conducting demo sessions with educators to gather feedback.

14
  Deployment of a fully functional Flask server for local or cloud access.

7. Future-Ready Enhancements (Planned Scope)

 Multilingual OCR for broader applicability.
 Diagram and equation recognition in future versions.
 AI-generated feedback and topic mastery analysis for students.

In summary, the scope of the AI-Driven Handwritten Evaluation System

project is vast, with current features focused on core evaluation automation and
future-ready pathways for scaling into a full educational assessment suite. It is
designed to empower educators with intelligent tools, promote inclusive digital
access, and modernize academic processes through AI and OCR technologies

15
 CHAPTER 5
RESULTS
The AI-Driven Handwritten Evaluation System has demonstrated significant
advancements in transforming traditional examination assessment processes
into a more intelligent, efficient, and unbiased digital solution. Through the
successful integration of Optical Character Recognition (OCR), Artificial
Intelligence (AI), and Natural Language Processing (NLP), the project has
achieved high levels of automation and accuracy in evaluating student answer
sheets.
By leveraging OCR technologies such as EasyOCR and Microsoft’s TrOCR,
the system accurately extracts handwritten content from scanned images of
student exam papers. This extracted content is then analyzed using text
similarity algorithms (such as RapidFuzz) to compare against the predefined
model answer keys. The implementation of this AI-powered evaluation process
has resulted in significant time savings and improved consistency in grading,
while reducing human error and subjectivity.
Furthermore, the project includes a user-friendly Flask-based web interface that
allows teachers and administrators to upload documents, initiate evaluations,
and download result reports in PDF or CSV formats. The integration of a
MySQL database enables efficient result storage, retrieval, and filtering for
individual students or batches.
Initial pilot testing was conducted using real-world exam data to evaluate the
system’s performance. The results indicate that the system is capable of
providing timely, reliable, and scalable assessment support, particularly
valuable for institutions handling large volumes of exam papers. Feedback from
test users was largely positive, praising the speed, accessibility, and fairness of
the evaluation process.
These outcomes signal promising strides toward achieving the project's primary
goal of modernizing the exam evaluation system through AI, reducing faculty
workload, and enhancing educational feedback mechanisms.

5.1 PREDICTIONS
Predictions for the AI-Driven Handwritten Evaluation System are as follows:

16
Students:
The project is expected to significantly improve the speed and transparency of
exam result processing. Students will benefit from quicker access to their results
and consistent, unbiased grading. With future enhancements, the system may
also offer feedback on handwriting clarity and content quality, further assisting
students in their academic growth.
 Prediction: Institutions using this system could expect a 30-40%
reduction in the time taken to evaluate handwritten exams, enabling
faster result announcements and quicker feedback loops for students.
Teachers:
Teachers will experience substantial reductions in manual workload, allowing
them to focus more on personalized instruction and content development rather
than time-consuming evaluations.
 Prediction: Educators could see a 50-60% reduction in time spent on
exam correction, with accuracy and consistency improving by 15-20%
due to the system’s AI-driven evaluation.
Institutions:
Educational institutions adopting this system will not only enhance their
assessment standards but also modernize their academic operations, potentially
improving institutional reputation and efficiency.
 Prediction: Widespread implementation could lead to overall
operational efficiency improvements of 25-30%, especially during
peak exam periods.
These predictions reflect the potential transformative impact of the AI-Driven
Handwritten Evaluation System on the educational sector. While actual
results may vary based on usage conditions and future upgrades, the project lays
the groundwork for a future-ready, AI-empowered academic evaluation process
that is fair, fast, and inclusive.

5.2 OUTPUTS

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Fig 5.2.1 Login page for uploading

18
 Fig 5.2.2 Uploading Student Answer Sheet

Fig 5.2.3 Conversion of Student answer sheet

19
 Fig 5.2.4 Text Extraction

Fig 5.2.5 Results of mark allocation

20
 Fig 5.2.5 Accuracy Calculation

5.3 SOURCE CODE

File Structure:
cpp
CopyEdit
handwritten_eval/
│
├── app.py
├── templates/
│ └── index.html
│ └── result.html
├── static/
│ └── style.css
├── uploads/
│ └── exam_papers/
│ └── answer_keys/

21
└── requirements.txt
1. requirements.txt
text
CopyEdit
Flask==2.2.2
easyocr==1.5.0
torch==1.10.0
rapidfuzz==2.0.0
mysql-connector-python==8.0.29
Pillow==9.1.0
reportlab==3.6.1

2. app.py (Main Flask Application)

python
CopyEdit
from flask import Flask, request, render_template, send_file
import easyocr
from rapidfuzz import fuzz
import mysql.connector
from PIL import Image
from io import BytesIO
from reportlab.pdfgen import canvas

app = Flask(__name__)

# Initialize OCR Reader

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ocr_reader = easyocr.Reader(['en'])

# Database connection
def get_db_connection():
conn = mysql.connector.connect(
host="localhost",
user="root",
password="your_password",
database="handwritten_eval"
)
return conn

# Home Route
@app.route('/')
def home():
return render_template('index.html')

# Upload Exam Paper and Answer Key

@app.route('/upload', methods=['POST'])
def upload():
exam_paper = request.files['exam_paper']
answer_key = request.files['answer_key']

# Process OCR for exam paper

exam_paper_text = extract_text_from_image(exam_paper)

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 # Process OCR for answer key
answer_key_text = extract_text_from_image(answer_key)

# Compare Text and Calculate Marks

marks = compare_text(exam_paper_text, answer_key_text)

# Save Data to Database

conn = get_db_connection()
cursor = conn.cursor()
cursor.execute("INSERT INTO results (exam_paper, answer_key, marks)
VALUES (%s, %s, %s)",
(exam_paper.filename, answer_key.filename, marks))
conn.commit()
conn.close()

return render_template('result.html', marks=marks)

# OCR Text Extraction

def extract_text_from_image(file):
image = Image.open(file)
text = ocr_reader.readtext(image)
extracted_text = " ".join([item[1] for item in text])
return extracted_text

# Compare Exam Paper Text with Answer Key

def compare_text(exam_text, answer_text):

24
 similarity = fuzz.ratio(exam_text, answer_text)
marks = calculate_marks(similarity)
return marks

# Calculate Marks Based on Similarity

def calculate_marks(similarity):
if similarity > 90:
return 10
elif similarity > 70:
return 7
elif similarity > 50:
return 5
else:
return 2

# Generate PDF Report

@app.route('/download_report/<int:result_id>')
def download_report(result_id):
conn = get_db_connection()
cursor = conn.cursor()
cursor.execute("SELECT * FROM results WHERE id = %s", (result_id,))
result = cursor.fetchone()
conn.close()

# Generate PDF
pdf_filename = f"report_{result_id}.pdf"
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 packet = BytesIO()
can = canvas.Canvas(packet)
can.drawString(100, 800, f"Exam Paper: {result[1]}")
can.drawString(100, 780, f"Answer Key: {result[2]}")
can.drawString(100, 760, f"Marks: {result[3]}")
can.save()

packet.seek(0)
return send_file(packet, as_attachment=True, download_name=pdf_filename)

if __name__ == '__main__':
app.run(debug=True)

3. index.html (Homepage Template)

html
CopyEdit
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Handwritten Evaluation System</title>
<link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
</head>
<body>
26
 <h1>AI Handwritten Evaluation System</h1>
<form action="/upload" method="POST" enctype="multipart/form-data">
<label for="exam_paper">Upload Exam Paper:</label>
<input type="file" name="exam_paper" required><br><br>
<label for="answer_key">Upload Answer Key:</label>
<input type="file" name="answer_key" required><br><br>
<button type="submit">Submit</button>
</form>
</body>
</html>

4. result.html (Result Page Template)

html
CopyEdit
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Evaluation Result</title>
</head>
<body>
<h1>Evaluation Result</h1>
<p>Marks: {{ marks }}</p>
<a href="/download_report/{{ result_id }}">Download Report</a>
27
</body>
</html>

5. style.css (Basic Styling for the Form)

css
CopyEdit
body {
font-family: Arial, sans-serif;
text-align: center;
}

form {
margin-top: 20px;
}

input[type="file"] {
margin: 10px;
}

button {
padding: 10px 20px;
background-color: #4CAF50;
color: white;
border: none;
cursor: pointer;
28
}

6. Database Setup

sql
CopyEdit
CREATE DATABASE handwritten_eval;

USE handwritten_eval;

CREATE TABLE results (

id INT AUTO_INCREMENT PRIMARY KEY,
exam_paper VARCHAR(255),
answer_key VARCHAR(255),
marks INT
);
main.py (Flask Application Code)

python
CopyEdit
from flask import Flask, request, render_template
import easyocr
from rapidfuzz import fuzz
from PIL import Image

# Initialize Flask app

29
app = Flask(__name__)

# Initialize OCR reader (for English language)

ocr_reader = easyocr.Reader(['en'])

# Function to extract text from an image (OCR)

def extract_text_from_image(image_file):
# Open the image file
image = Image.open(image_file)

# Perform OCR on the image to extract text

ocr_result = ocr_reader.readtext(image)

# Extract the text from OCR result

extracted_text = " ".join([item[1] for item in ocr_result])

return extracted_text

# Function to compare exam paper text with answer key text

def compare_text_accuracy(exam_text, answer_text):
# Calculate similarity score (0 to 100)
similarity = fuzz.ratio(exam_text, answer_text)
return similarity

# Function to assign marks based on similarity score

def assign_marks(similarity_score):
30
 if similarity_score >= 90:
return 10
elif similarity_score >= 70:
return 7
elif similarity_score >= 50:
return 5
else:
return 2

# Route to upload exam paper and answer key

@app.route('/')
def home():
return render_template('index.html')

# Route to handle file upload and process the evaluation

@app.route('/upload', methods=['POST'])
def upload():
# Get the uploaded files (exam paper and answer key)
exam_paper = request.files['exam_paper']
answer_key = request.files['answer_key']

# Extract text from the uploaded exam paper and answer key
exam_paper_text = extract_text_from_image(exam_paper)
answer_key_text = extract_text_from_image(answer_key)

# Compare the extracted texts and calculate similarity

31
 similarity = compare_text_accuracy(exam_paper_text, answer_key_text)

# Assign marks based on similarity score

marks = assign_marks(similarity)

# Render result page with marks

return render_template('result.html', marks=marks)

if __name__ == '__main__':
app.run(debug=True)

Text Detection (using EasyOCR)

from flask import Flask, request, render_template

from rapidfuzz import fuzz
import easyocr
from PIL import Image

# Initialize Flask app

app = Flask(__name__)

# Initialize OCR reader (for English language)

ocr_reader = easyocr.Reader(['en'])

# Function to extract text from an image

def extract_text_from_image(image_file):
32
 image = Image.open(image_file)
ocr_result = ocr_reader.readtext(image)
extracted_text = " ".join([item[1] for item in ocr_result])
return extracted_text

# Function to compare texts and calculate similarity

def compare_text_accuracy(exam_text, answer_text):
return fuzz.ratio(exam_text, answer_text)

# Function to assign marks based on similarity score

def assign_marks(similarity_score):
if similarity_score >= 90:
return 10
elif similarity_score >= 70:
return 7
elif similarity_score >= 50:
return 5
else:
return 2

# Upload route to handle files

@app.route('/upload', methods=['POST'])
def upload():
# Get the uploaded files (exam paper and answer key)
exam_paper = request.files['exam_paper']
answer_key = request.files['answer_key']
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 # Extract text from the uploaded files
exam_paper_text = extract_text_from_image(exam_paper)
answer_key_text = extract_text_from_image(answer_key)

# Compare the extracted texts and calculate similarity

similarity = compare_text_accuracy(exam_paper_text, answer_key_text)

# Assign marks based on similarity score

marks = assign_marks(similarity)

# Display result
return render_template('result.html', marks=marks)

if __name__ == '__main__':
app.run(debug=True)

Graph Generation:

import matplotlib.pyplot as plt

from flask import Flask, request, render_template, send_file
import easyocr
from rapidfuzz import fuzz
from PIL import Image
import io

34
# Initialize Flask app
app = Flask(__name__)

# Initialize OCR reader (for English language)

ocr_reader = easyocr.Reader(['en'])

# Function to extract text from an image (OCR)

def extract_text_from_image(image_file):
# Open the image file
image = Image.open(image_file)

# Perform OCR on the image to extract text

ocr_result = ocr_reader.readtext(image)

# Extract the text from OCR result

extracted_text = " ".join([item[1] for item in ocr_result])

return extracted_text

# Function to compare exam paper text with answer key text

def compare_text_accuracy(exam_text, answer_text):
# Calculate similarity score (0 to 100)
similarity = fuzz.ratio(exam_text, answer_text)
return similarity

# Function to assign marks based on similarity score

35
def assign_marks(similarity_score):
if similarity_score >= 90:
return 10
elif similarity_score >= 70:
return 7
elif similarity_score >= 50:
return 5
else:
return 2

# Route to upload exam paper and answer key

@app.route('/')
def home():
return render_template('index.html')

# Route to handle file upload and process the evaluation

@app.route('/upload', methods=['POST'])
def upload():
# Get the uploaded files (exam paper and answer key)
exam_paper = request.files['exam_paper']
answer_key = request.files['answer_key']

# Extract text from the uploaded exam paper and answer key
exam_paper_text = extract_text_from_image(exam_paper)
answer_key_text = extract_text_from_image(answer_key)

36
 # Compare the extracted texts and calculate similarity
similarity = compare_text_accuracy(exam_paper_text, answer_key_text)

# Assign marks based on similarity score

marks = assign_marks(similarity)

# Generate the graph for marks distribution

generate_marks_graph([marks])

# Render result page with marks

return render_template('result.html', marks=marks)

# Function to generate a graph of marks

def generate_marks_graph(marks_list):
# Create a new figure
plt.figure(figsize=(6, 4))

# Plot the bar graph for marks distribution

plt.bar(['Marks'], marks_list, color='skyblue')

# Set graph title and labels

plt.title('Marks Distribution', fontsize=14)
plt.ylabel('Marks', fontsize=12)
plt.xlabel('Evaluation', fontsize=12)

# Save the plot as a PNG image in memory

37
 img = io.BytesIO()
plt.savefig(img, format='png')
img.seek(0)

# Save the image globally (if needed for further use)

with open("static/marks_graph.png", "wb") as f:
f.write(img.getbuffer())

# Clear the current plot to free memory for the next graph
plt.clf()

# Serve the graph image (if needed)

@app.route('/get_graph')
def get_graph():
return send_file('static/marks_graph.png', mimetype='image/png')

if __name__ == '__main__':
app.run(debug=True)
Explanation of the Changes:
generate_marks_graph():

This function generates a simple bar chart showing the marks obtained by the
student.

The marks are plotted as a bar using Matplotlib.

38
The chart is saved as an image in the static folder (static/marks_graph.png).

Serving the Graph:

The function get_graph() is added to serve the graph image on a URL endpoint
(/get_graph).

You can display this image on the result page, if desired.

Saving the Graph:

The generated graph is saved in the static/ folder. This folder is typically used
for serving static files in Flask, such as images, CSS, and JavaScript.

Displaying the Graph in result.html:

You can now display the graph in your result.html by embedding the image.

html
Copy
Edit
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Evaluation Result</title>

39
</head>
<body>
<h1>Evaluation Result</h1>
<p>Marks: {{ marks }}</p>
<h2>Marks Distribution</h2>
<img src="{{ url_for('get_graph') }}" alt="Marks Graph" />
</body>
</html>

OUTPUT OF ACCURACY AND GRAPH :

40
Explanation of Code:
1. Flask Routes:
o /: The home route where users can upload exam papers and answer
keys.

41
 o /upload: This route processes the uploaded files, performs OCR on
both the exam paper and the answer key, and calcul
o ates marks based on text similarity.
o /download_report/<int:result_id>: This generates a downloadable
PDF report of the results.
2. OCR Integration (EasyOCR):
o The easyocr.Reader is used to extract text from the uploaded exam
papers and answer keys.
3. Text Comparison (RapidFuzz):
o The similarity between the exam paper's extracted text and the
answer key's text is calculated using RapidFuzz's fuzz.ratio method.
Marks are awarded based on the similarity percentage.
4. Database (MySQL):
o The results (exam paper name, answer key name, and marks) are
stored in a MySQL database.
5. Report Generation (PDF):
o The reportlab library is used to generate a PDF report with the
exam paper name, answer key name, and the obtained marks.

Component Purpose Library Used

Handles UI, uploads, and

Flask flask
routing

Extract text from

OCR handwritten exam and easyocr, PIL
answer key

Text Compare extracted text and

rapidfuzz
Comparison compute similarity score

Marks Assign marks based on Custom logic

42
Component Purpose Library Used

Allocation similarity

Store results for future

Database mysql.connector
reference

Generate downloadable
PDF Report reportlab
evaluation result

Graphical display of marks

Visualization matplotlib
(optional)

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 CHAPTER 6
CONCLUSION AND FUTURE WORK
6.1 CONCLUSION
The AI-Driven Handwritten Evaluation System successfully demonstrates the
potential of integrating Artificial Intelligence, Optical Character Recognition
(OCR), and Machine Learning (ML) to automate and improve traditional
academic assessment methods. This project streamlines the evaluation process
of handwritten student answer sheets by extracting content using OCR tools
(EasyOCR and TrOCR), comparing the results using semantic similarity
measures (RapidFuzz), and presenting scores through a simple web interface
built with Flask.

The system reduces the time and effort required by educators, increases the
consistency of grading, and ensures unbiased evaluations. Teachers can upload
exams, view evaluated answers instantly, and export result reports in multiple
formats. The database integration ensures that results are safely stored and
easily retrieved.

The system supports a wide range of handwriting styles and continues to

improve with ongoing updates. The positive outcomes from the pilot phase and
initial deployment confirm the effectiveness, reliability, and potential impact of
the system in modern education.

In conclusion, the project represents a meaningful step toward digitizing and

modernizing evaluation systems. It supports educators, simplifies workflows,
and provides a better academic experience for students by ensuring timely and
fair results.

44
6.2 FUTURE WORK
To expand the capabilities and adaptability of the system, the following areas
are proposed for future development:

✅ 1. Multilingual and Script Support

Incorporating OCR for regional languages and multiple scripts (e.g., Hindi,
Tamil) to broaden the system's usability across diverse regions.

✅ 2. Diagram and Mathematical Equation Evaluation

Future models may support the recognition and evaluation of diagrams, charts,
and mathematical symbols using image recognition and symbolic processing.

✅ 3. Automated Feedback Generation

Using NLP to provide detailed feedback for students, suggesting improvements
and highlighting key missing concepts.

✅ 4. Enhanced AI Models
Implementing deep learning-based models specifically trained on educational
datasets to improve handwriting interpretation and accuracy.

✅ 5. Learning Management System (LMS) Integration

Integrating with platforms like Moodle or Google Classroom for automated
result posting, grading, and analytics.

✅ 6. Mobile Compatibility
Creating a mobile-friendly version or dedicated app for teachers and students to
upload or view results on the go.

✅ 7. Real-Time Performance Analytics

Developing dashboards that visualize class performance, weak areas, and
improvement suggestions.

✅ 8. Security & Privacy

Strengthening data protection through encryption, access control, and
compliance with educational data privacy standards.

45
 REFERENCES

Smith, R. (2007). An Overview of the Tesseract OCR Engine. Proceedings

of the Ninth International Conference on Document Analysis and Recognition
(ICDAR), 2, 629–633. https://doi.org/10.1109/ICDAR.2007.4376991
Chen, X., Jin, L., Zhu, Y., & Luo, C. (2021). TrOCR: Transformer-based
OCR for Handwritten Document Recognition. arXiv preprint arXiv:2109.10282.
Rao, M., & Sinha, A. (2019). Automated Evaluation System for Descriptive
Answers Using Natural Language Processing. Procedia Computer Science, 152,
123–130.
Sadek, C., Mahmoud, T., & Elaraby, I. (2019). Smart Evaluation System for
Student Descriptive Answers Based on Semantic Analysis. International
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Gupta, H., & Verma, S. (2020). Text Similarity and Plagiarism Detection
Using NLP Techniques. International Journal of Scientific & Technology
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., et al. (2017). Attention
is All You Need. Advances in Neural Information Processing Systems.
Jain, R., & Arora, A. (2022). An Intelligent AI-based Framework for
Evaluating Handwritten Exams. International Journal of Emerging
Technologies in Learning (iJET), 17(9), 45–58.
Baek, J., Kim, G., Lee, J., Park, S., Han, D., Yun, S., & Lee, H. (2019).
What Is Wrong With Scene Text Recognition Model Comparisons? ICCV,
4715–4723.
Li, M., Zhang, Z., & Lu, Y. (2021). Handwritten Text Recognition Based on
Deep Learning: A Review. Applied Sciences, 11(5), 2233.
Kaur, G., & Singh, P. (2021). A Review on OCR-Based Handwritten Text
Recognition Techniques. Journal of Engineering Research and Applications,
11(3), 05–1

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