The process of manually evaluating handwritten an-swer sheets is time-consuming, error-prone, and often influenced by subjective judgment. With advancements in deep learning and computer vision, automated grading systems are emerg-ing as efficient alternatives to traditional evaluation methods. CogniGrade presents an AI-driven approach for the automated assessment of handwritten responses, combining optical character recognition (OCR), diagram interpretation, and semantic anal-ysis. The system first digitizes handwritten text using advanced OCR models such as CRAFT for text detection and TrOCR for text recognition. For diagram-based questions, a YOLOv5-based object detection model identifies and interprets various flowchart components. Both the recognized textual and visual data are semantically analyzed through a language model to generate an accurate and consistent evaluation score. The proposed system aims to minimize human effort, enhance grading consistency, and significantly reduce evaluation time, thereby improving the efficiency and fairness of academic assessments.
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CogniGrade: An AI-Powered System for Automated Grading of Handwritten Answers
Semantic Scholar · 2026
Abstract
The process of manually evaluating handwritten an-swer sheets is time-consuming, error-prone, and often influenced by subjective judgment. With advancements in deep learning and computer vision, automated grading systems are emerg-ing as efficient alternatives to traditional evaluation methods. CogniGrade presents an AI-driven approach for the automated assessment of handwritten responses, combining optical character recognition (OCR), diagram interpretation, and semantic anal-ysis. The system first digitizes handwritten text using advanced OCR models such as CRAFT for text detection and TrOCR for text recognition. For diagram-based questions, a YOLOv5-based object detection model identifies and interprets various flowchart components. Both the recognized textual and visual data are semantically analyzed through a language model to generate an accurate and consistent evaluation score. The proposed system aims to minimize human effort, enhance grading consistency, and significantly reduce evaluation time, thereby improving the efficiency and fairness of academic assessments.