A Review on Automatic Subjective Answer Evaluation

Offline handwritten text recognition from images is a long-standing research topic for researchers trying to digitize a large number of hand-scanned documents. We propose a novel neural network architecture that integrates feature extraction, sequence learning, and transcription into a unified framework. The documentation process converts paper-based and handwritten text into electronic information. Extracting text lines, keywords, and images from such a complex document can be a difficult task. Identifying text lines from handwritten or printed document images is a crucial stage in the optical character recognition (OCR) system process. This paper addresses the challenge of evaluating handwritten subjective answers using deep learning techniques. Traditional manual evaluation methods are prone to human error, time-consuming, and subjective. Automating the evaluation process ensures objectivity and efficiency, particularly in educational settings. Deep learning models, like convolutional neural networks (CNNs), are used in the proposed system to read handwritten text. Machine learning (SVM or NB) or transformer architectures are used to understand and rate the answers' semantic relevance. Natural Language Processing (NLP) has created an opportunity for computers to learn about written text data and make important decisions based on the learned model. We designed the system to handle varying writing styles and assess answers based on predefined marking schemes.