Automatic Subjective Answer Evaluation

Abstract: Automatic evaluation of subjective answers has become a vital area of research due to its potential to reduce the manual effort required in educational assessments. This paper presents an advanced system for the automatic evaluation of handwritten subjective answers, integrating Optical Character Recognition (OCR), Natural Language Processing (NLP), and semantic similarity techniques. The system employs the Google Cloud Vision API to extract textual data from handwritten answer sheets with high accuracy. Extracted responses undergo preprocessing, including spell correction, and are semantically compared with ideal answers using both BERT and fine-tuned SBERT models. To enhance grading reliability, a custom contrastive learning mechanism is implemented for SBERT fine-tuning, using student-ideal answer pairs. The evaluation is performed via a Flask-based backend, which also supports training workflows through API endpoints. Feedback and marks are generated based on semantic similarity and model confidence. This system demonstrates an effective solution for automating subjective answer assessment with a high degree of flexibility and accuracy, particularly for handwritten inputs. The system supports both real-time evaluation and model customization, offering educators the flexibility to retrain models using domainspecific datasets. A user-friendly web interface allows for seamless uploading of answer images, configuration of model settings, and visualization of results. Additionally, the system integrates a secure user authentication module for access control, enabling personalized model training and usage history tracking. Experimental results demonstrate that the fine-tuned SBERT model significantly improves semantic alignment with ground-truth answers, especially in the context of varied handwriting styles and non-standard grammar.