Marksheet_Analysis_using_ocr- AI AN

1. D. Y. PATIL COLLEGE OF ENGINEERING, AKURDI, PUNE-44
Department of Artificial Intelligence & Data Science
BE Project Stage - I (A.Y. 2023-24 SEM I)
Presentation On
Marksheet Analysis using OCR
Team Members : Guide
• Tushar Patle (BEAIDB09) Prof. Pranjali Bahalkar
• Swaraj Kakade (BEAIDB18)
• Yash Nathe (BEAIDB32)
• Rohan Dable (BEAIDB65)

2. PROBLEM STATEMENT:
Design an OCR-based Marksheet Analysis System tailored to a specific set of fixed
marksheet templates. The system should be able to precisely extract essential
information from scanned or photographed marksheet documents, including student
details such as names and roll numbers, subject names and corresponding scores,
grades, and any additional relevant data. The solution should demonstrate a high level
of accuracy in OCR recognition, ensuring seamless data extraction even with variations
in scanned quality. The extracted data should be processed automatically to calculate
cumulative scores, averages, and generate comprehensive individual or aggregated
performance reports. User-friendly interfaces for input, verification, and result
visualization should be incorporated to facilitate easy usage by educational institutions,
reducing manual effort and potential errors in marksheet analysis.

3. ABSTRACT:
• The ”Marksheet Analysis using OCR” project represents a significant advancement
in the field of educational data management. In today’s data-driven educational
landscape, the project aims to streamline the process of extracting, storing, and
analyzing marksheet data, which is integral to assessing student performance and
supporting administrative decision-making.
• The system incorporates a robust database management component, facilitating the
secure storage and retrieval of marksheet data.
• Security is a paramount concern in this project, with data encryption, user
authentication, and access controls in place to safeguard sensitive information.
• Additionally, the system is designed for scalability, accommodating the growing
needs of educational institutions without compromising performance.

4. INTRODUCTION:
• Manual Data Entry Challenges : Educational institutions often handle a significant
volume of mark sheets for grading and record-keeping. Manually entering this data
into digital systems is not only time-consuming but also prone to human errors,
which can have a significant impact on students’ academic records.
• Data-Driven Decision-Making : Educational institutions increasingly rely on data to
make informed decisions about curriculum, student support, and performance
assessment. The automation of marksheet analysis provides reliable data that can be
used for these purposes.
• Time and Resource Efficiency : Automating the data entry and analysis process frees
up valuable time and resources that can be allocated to more productive and
strategic tasks in educational administration.

5. OBJECTIVE:
1. Personal Performance Dashboard: Develop an intuitive dashboard for students to track
individual subject scores, grades, and cumulative scores over different semesters, promoting
self-awareness and academic improvement.
2. Efficient Teacher Data Entry: Create a streamlined data entry interface for teachers to input
student marks, integrating validation checks to ensure accuracy and minimize data entry
errors.
3. Excel Data Export for Analysis: Enable teachers to generate and download Excel-formatted
reports containing student performance details, facilitating in-depth analysis and informed
decision-making
4. Comprehensive Administrative Insights: Design specialized dashboards for Heads of
Departments (HoDs) and the Principal to access aggregated performance data, enabling them to
visualize trends, conduct comparative analysis, and make strategic decisions.
5. Enhanced Data Accuracy and Visualization: Implement validation mechanisms for accurate
data extraction, calculations, and dynamic visualizations for all user roles, improving data
integrity and promoting data-driven actions.

6. METHODOLOGY:

12. LITERATURE REVIEW:
Advantages Disadvantages
High Accuracy : OCR ensure precise data
extraction, reducing errors in student details and
scores.
Template Dependency: Template-based OCR may
face challenges if mark sheets deviate significantly
from the predefined templates.
Efficiency Gains: Automation reduces manual
effort in data entry and analysis, leading to time
savings.
Initial Setup: Designing and implementing
templates require an initial investment of time
and resources.
Scalability: The system can handle a large volume
of mark sheets efficiently, supporting scalability.
Training Data: OCR systems may require
extensive training data for optimal performance,
which can be resource-intensive.
Customization: The system can be tailored to
specific educational institutions and their mark
sheet formats.
Variability: Handling variations in document
quality and format may pose challenges, affecting
OCR accuracy.
Comprehensive Reporting: Automated analysis
facilitates the generation of detailed individual
and aggregate performance reports.
Integration Complexity: Integrating the OCR
system with existing educational software may
require technical expertise.
User-Friendly Interfaces: Intuitive interfaces for
input, verification, and result visualization
enhance user experience.
Maintenance: Regular updates and maintenance
are necessary to adapt to changes in mark sheet
formats or OCR technology.

13. REFERENCES :
1) Efficient, Lexicon-Free OCR using Deep Learning By Marcin Namysl, Iuliu Konya
Link : - https://arxiv.org/abs/1906.01969
2) Marksheet Recognition Methodology-Correlation
Link :- https://docplayer.net/203648389-Marksheet-recognition-methodology-correlation.html
3) Deep Learning for Historical Document Analysis and Recognition—A Survey
Link:- https://www.mdpi.com/2313-433X/6/10/110
4) ‘Image text extraction with OCR using Open CV and PyTesseract’
Link :- https://datascience-learners.medium.com/image-text-extraction-with-ocr-using-open-cv-
and-pytesseract-b3f4696a6de1
5) An OCR based automated method for textual analysis of questionnaires
Link :- https://www.ijcse.com/docs/INDJCSE17-08-02-033.pdf

14. CONCLUSION:
Marksheet analysis using OCR has successfully demonstrated the potential of Optical Character
Recognition technology in automating the tedious task of extracting information from mark sheets. By
leveraging OCR, we have achieved greater accuracy and efficiency in data entry and analysis, reducing the
chances of human errors and saving valuable time.
Furthermore, this endeavor highlights the importance of technology in streamlining administrative
processes and enhancing data management in the education sector. As we continue to advance in the fields
of coding and mathematics, we can expect even more sophisticated applications of OCR and data analysis
to revolutionize how we handle educational data, ultimately benefiting students, teachers, and institutions
alike.