One-Word Answer Correction using Deep Learning Models and OCR

• Published in: International journal of recent technology and engineering Vol. 9; no. 2; pp. 679 - 682
• Main Authors: Devan, K. P. K., P, Sruthi Prabakaran, S, Tamizhazhagan, S, Vaishnavi
• Format: Journal Article
• Language: English
• Published: 30.07.2020
• ISSN: 2277-3878; 2277-3878
• DOI: 10.35940/ijrte.B3849.079220

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 time-consuming 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 CNN-RNN hybrid model and Optical Character Recognition (OCR) to predict a score for one-word answers.