Epistemological and Axiological Analysis of ResNet18-Based Dysgraphia Classification
DOI:
https://doi.org/10.29407/gj.v10i1.27419Keywords:
dysgraphia classification, ResNet18, handwriting, epistemology, axiologyAbstract
Based on an ontological perspective, there is a gap in feature representation and in binary dysgraphia classification using ResNet18, an area that has not been explored simultaneously. Thus, our contribution is an analysis of research on dysgraphia classification using ResNet18 that employs epistemological and axiological approaches. ResNet18 was chosen as the backbone of the proposed framework because it has shortcut connections that can degrade residues into useless features. As a representation of new knowledge, ResNet18 was pre-trained on ImageNet. Classification was tested on challenging word assignments, comprising 145 dysgraphia images and 188 non-dysgraphia images. Epoch trials were conducted to find the best architecture. The results showed that ResNet18 at epoch 10 achieved the best performance in binary classification, with a recall of up to 93.55%. This indicates that ResNet18 is sensitive to recognizing dysgraphia classes. Challenges outlined in this study serve as a foundation for further research.
References
[1] C. Weinschenk and H. Harbauer, “Diagnosis and treatment of minor cerebral damage in early childhood; DIAGNOSE UND BEHANDLUNG DER LEICHTEN FRUHKINDLICHEN HIRNSCHADIGUNGEN,” Deutsches Arzteblatt, vol. 71, no. 26, pp. 1907–1908, 1974.
[2] B. Manimekala, D. Umamaheswari, J. Rozario, M. Kannan, and P. M. Savitha, “Dysgraphia Disorder Detection and Classification Using Deep Learning Technique,” SN Computer Science, vol. 6, no. 3, 2025, doi: 10.1007/s42979-025-03825-5.
[3] B. Moss, J. Marshall, C. Woolf, and K. Hilari, “Can a writing intervention using mainstream Assistive Technology software compensate for dysgraphia and support reading comprehension for people with aphasia?,” International Journal of Language and Communication Disorders, vol. 59, no. 3, pp. 1090–1109, 2024, doi: 10.1111/1460-6984.12975.
[4] B. L. Tee et al., “Dysgraphia Phenotypes in Native Chinese Speakers With Primary Progressive Aphasia,” Neurology, vol. 98, no. 22, pp. E2245–E2257, 2022, doi: 10.1212/WNL.0000000000200350.
[5] J. Kunhoth, S. A. Al-Madeed, S. Kunhoth, Y. Akbari, and M. M. Saleh, “Automated systems for diagnosis of dysgraphia in children: a survey and novel framework,” International Journal on Document Analysis and Recognition, vol. 27, no. 4, pp. 707–735, 2024, doi: 10.1007/s10032-024-00464-z.
[6] I. Mudrenko, V. S. Lychko, and D. D. Sotnikov, “Speech Disorders In Patients With Suicidal Behaviour Accompanied By Different Types Of Dementia,” Eastern Ukrainian Medical Journal, vol. 10, no. 3, pp. 233–240, 2022, doi: 10.21272/eumj.2022;10(3):233-240.
[7] P. Zham et al., “Effect of levodopa on handwriting tasks of different complexity in Parkinson’s disease: a kinematic study,” Journal of Neurology, vol. 266, no. 6, pp. 1376–1382, 2019, doi: 10.1007/s00415-019-09268-2.
[8] A. Anggrawan, H. Hairani, C. Satria, and A. D. Dayani, “Diagnosing Learning Disorders in Children: A Comparison of Certainty Factor and Dempster-Shafer Methods,” International Journal of Information and Education Technology, vol. 13, no. 9, pp. 1422–1429, 2023, doi: 10.18178/ijiet.2023.13.9.1945.
[9] R. Brunsdon, M. Coltheart, and L. A. Nickels, “Treatment of irregular word spelling in developmental surface dysgraphia,” Cognitive Neuropsychology, vol. 22, no. 2, pp. 213–251, 2005, doi: 10.1080/02643290442000077.
[10] A. Anggrawan, H. Hairani, C. Satria, and A. D. Dayani, “Diagnosing Learning Disorders in Children: A Comparison of Certainty Factor and Dempster-Shafer Methods,” International Journal of Information and Education Technology, vol. 13, no. 9, pp. 1422–1429, 2023, doi: 10.18178/ijiet.2023.13.9.1945.
[11] S. Ceccacci, A. Taddei, N. Del Bianco, C. Giaconi, D. Forteza Forteza, and F. Moreno-Tallón, “Preventing Dysgraphia: Early Observation Protocols and a Technological Framework for Monitoring and Enhancing Graphomotor Skills,” Information (Switzerland), vol. 15, no. 12, pp. 1–19, 2024, doi: 10.3390/info15120781.
[12] M. Kumar, P. Singh, and M. Kumar, “Behavioral intervention with fine motor training for dysgraphia in school going children,” International Journal of Current Research and Review, vol. 12, no. 18, pp. 180–187, 2020, doi: 10.31782/IJCRR.2020.121827.
[13] L. Andrade-Arenas and C. Yactayo-Arias, “Expert system for diagnosing learning disorders in children,” International Journal of Electrical and Computer Engineering, vol. 14, no. 3, pp. 2965–2975, 2024, doi: 10.11591/ijece.v14i3.pp2965-2975.
[14] G. Dimauro, V. Bevilacqua, L. Colizzi, and D. Di Pierro, “TestGraphia, a software system for the early diagnosis of dysgraphia,” IEEE Access, vol. 8, pp. 19564–19575, 2020, doi: 10.1109/ACCESS.2020.2968367.
[15] L. Deschamps et al., “Development of a Pre-Diagnosis Tool Based on Machine Learning Algorithms on the BHK Test to Improve the Diagnosis of Dysgraphia,” Advances in Artificial Intelligence and Machine Learning, vol. 1, no. 2, pp. 114–135, 2021, doi: 10.54364/AAIML.2021.1108.
[16] Y. Zhang et al., “A novel approach for handwriting recognition in Parkinson’s disease by combining flexible sensing with deep learning technologies,” Sensors and Actuators A: Physical, vol. 385, 2025, doi: 10.1016/j.sna.2025.116287.
[17] X. Chi, “Handwriting Recognition Based on Resnet-18,” pp. 456–459, 2021, doi: 10.1109/ICBASE53849.2021.00091.
[18] M. Maray et al., “Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition,” Computers, Materials and Continua, vol. 74, no. 3, pp. 5467–5482, 2022, doi: https://doi.org/10.32604/cmc.2023.033534.
[19] U.-H. Kim, Y. Hwang, S.-K. Lee, and J.-H. Kim, “Writing in the Air: Unconstrained Text Recognition from Finger Movement Using Spatio-Temporal Convolution,” IEEE Transactions on Artificial Intelligence, vol. 4, no. 6, pp. 1386–1398, 2023, doi: 10.1109/TAI.2022.3212981.
[20] A. M. Mutawa, M. Y. Allaho, and M. Al-Hajeri, “Machine Learning Approach for Arabic Handwritten Recognition,” Applied Sciences (Switzerland), vol. 14, no. 19, 2024, doi: 10.3390/app14199020.
[21] J. Kunhoth, M. Saleh, S. Al-Maadeed, P. Drotar, and Y. Akbari, “Grading of Developmental Dysgraphia Severity in Children: Multimodal Dataset and Classifier Fusion,” IEEE Transactions on Cognitive and Developmental Systems, vol. PP, pp. 1–15, 2025, doi: 10.1109/TCDS.2025.3597742.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Kartika Candra Kirana, Dr.Eng. Anik Nur Handayani , ST, MT, Prof. Dr.Ir. Syaad Patmanthara , M.Pd
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- The author grants the journal, the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
How to Cite
