One-Word Answer Correction using Deep Learning Models and OCR

Abstract

Examinations/Assessments are a way to assess the understanding of a student on a particular subject. Even today many educational organizations prefer to conduct exams by offline mode (pen and paper). And evaluating them is a timeconsuming process. There is no effectual model to evaluate Offline descriptive answers automatically. The traditional method involves staff assessing the content manually. In place of this process, a new approach using image captioning by using deep learning algorithms can be implemented. Handwritten Text Recognition (HTR) can be used to evaluate descriptive answers. One-word Answers captured as images are pre-processed to extract the text features using deep learning models and pytesseract. This paper presents a comparison between the CNNRNN hybrid model and Optical Character Recognition (OCR) to predict a score for one-word answers.

Text Extraction and Language Translation for Health Care and Clinical Records Using Deep Learning

“…The system accurately identi ed the characters with 92.7% accuracy. In addition to CNN, another neural network KNN was used by K. P. K Devan et al [3] where a system was proposed to evaluate questions that are answered in just one word. The system uses a hybrid model consisting of convolutional and recurrent neural networks.…”