Optical Character Recognition Based Answer Script Correction Using Deep Learning and Language Processing Algorithms

The rapid advancements in digital assessment systems have highlighted the need for efficient, automated methods of evaluating written examination scripts. The paper presents a new framework that leverages Optical Character Recognition (OCR), deep learning, as well as Natural Language Processing (NLP) to automate the correction and evaluation of handwritten answer scripts. The suggested system begins by utilizing OCR to digitize handwritten responses, converting them into machine readable text. Subsequently, advanced learning frameworks, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are employed to classify and recognize written characters with high accuracy, even in varying handwriting styles. Once digitized, the responses undergo semantic analysis and content evaluation using advanced NLP algorithms. These algorithms, integrated with transformer models like BERT (Bidirectional Encoder Representations from Transformers), evaluate the coherence, relevance, and correctness of answers by comparing them to model answers and predefined keywords. The system incorporates both syntactic and semantic matching techniques to ensure a comprehensive assessment of the answer content, while also analyzing grammar and language use. The proposed system can significantly streamline the educational assessment process, paving the way for intelligent and automated grading systems in academic and professional environments.