Development of Grader Provider System Using Deep Learning

In recent years, the demand for efficient evaluation systems in educational settings has surged, highlighting the need for automation in grading processes. This research presents a Grader pro (Automatic Answer Sheet Evaluation System) that integrates Optical Character Recognition (OCR) with Natural Language Processing (NLP) techniques, specifically leveraging transformer models. The proposed system aims to accurately assess handwritten answer sheets by first converting them into digital text using advanced Transformer based OCR /fine-tuned transformer for text extraction from hand writing. Following text extraction, the system employs NLP based transformer such as BERT model for key-factors evolutions to analyze and grade the responses based on predefined criteria and answer patterns. Our approach addresses key challenges such as varying handwriting styles, contextual understanding, and semantic analysis of answers. Pre-liminary results demonstrate significant improvements in evaluation speed and accuracy compared to traditional manual grading methods. This research not only contributes to the field of automated assessment but also paves the way for scalable solutions in educational technology, ultimately enhancing the learning experience by providing timely feedback to students. Future work will focus on refining the model’s capabilities and expanding its applicability to various educational contexts.