International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
OCR Result Analysis System Harsh Singh Bhatiya 1, Mayur Pawar2, Rohan Gaumgaonkar, Sharvan Dharme4, Rakesh Moharle5, Dr. Sushama Telrandhe6 1,2,3,4,U.G. Students, Department of Computer Science and Engineering, Gurunanak Institute of Engineering and
Technology, Nagpur, Maharashtra, India.
5 Assistant professor, Department of Computer Science and Engineering, Gurunanak Institute of Engineering and
Technology, Nagpur, Maharashtra, India.
6 Associate professor, Department of Computer Science and Engineering, Gurunanak Institute of Engineering and
Technology, Nagpur, Maharashtra, India. ---------------------------------------------------------------------***--------------------------------------------------------------------handwritten and digital text. By integrating deep learning, ABSTRACT- The OCR Result Analysis System automates student data entry, result generation, and report management using Optical Character Recognition (OCR). Student details such as Roll Number, Enrollment Number, Candidate Name, and academic records are extracted from scanned images or entered manually, then stored securely in a structured database. The system automatically generates Excel reports, including detailed sheets, summary reports, and category-wise analyses. A history module maintains all uploads and generated files for quick retrieval. With secure authentication and error-free processing, the system reduces manual workload, improves accuracy, and provides a reliable solution for educational result management.
the system recognizes exam-related data such as signatures and written information, stores it efficiently, generates automated graphs, and reduces manual effort throughout the exam management workflow.
1. INTRODUCTION
The work of Aarav T. Shaji, Justin Thomas T., Emils Saj, Vishnuprasad T. V., Deepa J., and Jibu Mathew (2024) focuses on document digitalization using Optical Character Recognition for student evaluation sheets. Their system converts handwritten exam digits and information into digital CSV format using CNN, helping streamline data management and reduce manual labor in the evaluation process.
In another study, Eman Shaikh, Iman Mohiuddin, Ayesha Manzoor, Ghazanfar Latif, and Nazeer uddin Mohammad (2019) developed an automated grading system for handwritten answer sheets using convolutional neural networks (CNN). Trained on a dataset of 250 answer sheets, the model achieved 92% accuracy and significantly reduced manual grading time by computing scores automatically.
The OCR Result Analysis System is designed to automate the management of student records and academic reports by utilizing Optical Character Recognition (OCR) technology. It allows users to securely log in and register before accessing features that let them manually input student details or upload scanned mark sheets for OCR processing. The system then stores the extracted data in a structured database, enabling efficient and accurate result generation. Result sheets are automatically created in Excel format, simplifying analysis and report sharing for teachers and administrators In addition to result generation, the system includes a History Module to record actions such as OCR uploads, result sheet generation, and file downloads. This module ensures transparency and accountability by allowing easy retrieval of previously generated files. By automating manual tasks, the system significantly reduces workload, improves operational efficiency, and provides a reliable, secure solution for schools, colleges, and universities to manage student records and generate academic reports.
Finally, Shams A. Al-Qaisy, Amal Hasan Qanaa, Rougar Khorshid, Mayar Mohamad, Salim Yahya Massad Kallul, and Ahmad Hussein Hassan (2025) emphasize enhancing security and data management in higher education through the integration of Cloud Content Management (CCM). Their study highlights challenges such as declining student engagement and resource accessibility, proposing that CCM solutions can improve collaboration, data sharing, storage, and accessibility, ultimately supporting better institutional operations.
3. METHODS AND MATERIALS
2. LITERATURE REVIEW
MATERIALS
The study by Mehdi Rizvi, Hasnain Raza, Shahab Maqsood, and Shan Afify (2019) presents an Optical Character Recognition (OCR)-based intelligent database management system designed for examination process control. Their work applies computer vision techniques, particularly OCR and object recognition, to automate the processing of
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