AN OCR Based Intelligent System for Prediction of Marks from Hardcopies
Volume 20, Issue 1, 2026
Download| Author(s): |
Jamil Ahmed* Department of Computer Science, SZABIST University, Larkana Campus, Pakistan, jamil.chandio@gmail.com Mohammad Muzamil Department of Computer Science, SZABIST University, Larkana Campus, Pakistan, soomromohammadmuzamil@gmail.com Shahzaib Shah Department of Computer Science, SZABIST University, Larkana Campus, Pakistan, syed669shahzaib@gmail.com Hassan Ali Department of Computer Science, SZABIST University, Larkana Campus, Pakistan, rajahassanali25@gmail.com |
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| Abstract | The manual assessment of student answer papers (MASAC) is a critical constraint in education, characterized by subjectivity, inconsistency, and major time delays. This paper presents the design and implementation of an AI-powered automated marks assessment system (PAMAS) that uses cutting- edge deep learning models to create an end-to-end grading pipeline. The system implements LightOnOCR (optical character recognition) for text extraction from images of handwritten (IoH) and typed answer sheets (AS), effectively converting visual data into machine-readable text. The extracted answers are then evaluated by the DeepSeek-R1 large language model, which performs a sophisticated semantic analysis against a provided model answer and marking scheme. A key innovation of our system is its dual-output capability: it generates both an accurate numerical score and detailed, personalized feedback for the student. Experimental results on a dataset of 150 short-answer questions demonstrate a scoring accuracy of 91.2% compared to human expert graders, with a dramatic reduction in grading time from hours to seconds per script. The system proves the viability of integrating specialized OCR and advanced LLMs to create a scalable, objective, and efficient assessment tool that benefits both educators and learners. |
| Keywords | automated Assessment, optical character recognition (OCR), large language model (LLM), educational technology |
| Year | 2026 |
| Volume | 20 |
| Issue | 1 |
| Type | Research paper, manuscript, article |
| Journal Name | Journal of Information & Communication Technology | Publisher Name | ILMA University | Jel Classification | - | DOI | - | ISSN no (E, Electronic) | 2075-7239 | ISSN no (P, Print) | 2415-0169 | Country | Pakistan | City | Karachi | Institution Type | University | Journal Type | Open Access | Manuscript Processing | Blind Peer Reviewed | Format | Paper Link | https://jict.ilmauniversity.edu.pk/journal/jict/20.1/3.pdf | Page | 22-27 |