A Step Toward an Automatic Handwritten Homework Grading System for Mathematics
Issued Date
2023-03-28
Resource Type
ISSN
1392124X
eISSN
2335884X
Scopus ID
2-s2.0-85152682815
Journal Title
Information Technology and Control
Volume
52
Issue
1
Start Page
169
End Page
184
Rights Holder(s)
SCOPUS
Bibliographic Citation
Information Technology and Control Vol.52 No.1 (2023) , 169-184
Suggested Citation
Chaowicharat E., Dejdumrong N. A Step Toward an Automatic Handwritten Homework Grading System for Mathematics. Information Technology and Control Vol.52 No.1 (2023) , 169-184. 184. doi:10.5755/j01.itc.52.1.32066 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/81770
Title
A Step Toward an Automatic Handwritten Homework Grading System for Mathematics
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
An automatic system that helps teachers and students verify the correctness of handwritten derivation in mathematics homework is proposed. The system acquires input image containing handwritten mathematical deri-vation. In our preliminary study, the system that comprises only mathematical expression recognition (MER) and computer algebra system (CAS) did not perform well due to high misrecognition rate. Therefore, our study focuses on fixing the misrecognized symbols by using symbols replacement and the surrounding information. If all the original mathematical expressions (MEs) in the derivation sequence are already equivalent, the derivation is marked as “correct”. Otherwise, the symbols with low recognition confidence will be replaced by other possible candidates to maximize the number of equivalent MEs in that derivation. If there is none of symbols replacement that makes every line equivalent, the derivation is marked as “incorrect”. The recursive expression tree comparison was applied to report the types of mistake for those problems marked as incorrect. Finally, the performance of the system was evaluated by the digitally generated dataset of 6,000 handwritten mathematical derivations. The results showed that the symbols replacement improve the F1-score of derivation step marking from 69.41 to 95.95 % for the addition/ subtraction dataset and from 61.45 to 89.95 % for the multiplication dataset when compared to the case of using raw recognized string without symbols replacement.