Open AccessOne-Word Answer Correction using Deep Learning Models and OCR
Author(s) -
K. P. K. Devan,
Senthil Kumari P,
S Tamizhazhagan,
S. Vaishnavi
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b3849.079220
Subject(s) - computer science , artificial intelligence , closed captioning , natural language processing , word (group theory) , deep learning , optical character recognition , process (computing) , subject (documents) , character (mathematics) , speech recognition , image (mathematics) , linguistics , world wide web , philosophy , geometry , mathematics , operating system
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.