PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Deep learning algorithms are increasingly being used to diagnose dysgraphia by concentrating on the issue of uneven handwriting characteristics, which is common among children in the early stage of basic learning of reading and writing skills. Convolutional Neural Network (CNN) is a deep learning model popular for classification tasks, including the dysgraphia detection process in assisting traditional diagnosis procedures. The CNN-based model is usually constructed by combining layers in the extraction network to capture the features of offline handwriting images before the classification network. However, concerns have been expressed regarding the limited study comparing the performance of the Directed Acyclic Graph (DAG) and Sequential Networks in handwriting-related studies in identifying dysgraphia. The proposed method was employed in this study to compare the two network structures utilized for feature extraction in classifying dysgraphia handwriting To eliminate this gap. Therefore, a new layer structure design in the Sequential and DAG networks was proposed to compare the performance of two feature extraction layers. The findings demonstrated that the DAG network outperforms the Sequential network with 1.75% higher accuracy in classification testing based on confusion matrix analysis. The study provides valuable insights into the efficiency of various network structures in recognizing inconsistencies identified in dysgraphia handwriting, underlining the need for additional research and improvement in this field. Subsequently, these findings highlight the necessity of deep learning approaches to advance dysgraphia identification and establish the framework for future research.

• Almisreb, A. A., Tahir, N. M., Turaev, S., Saleh, M. A., & Al Junid, S. A. M. (2022). Arabic handwriting classification using deep transfer learning techniques. Pertanika Journal of Science and Technology, 30(1), 641–654. https://doi.org/10.47836/PJST.30.1.35

• Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, Article 53. https://doi.org/10.1186/s40537-021-00444-8

• Asselborn, T., Chapatte, M., & Dillenbourg, P. (2020). Extending the spectrum of dysgraphia: A data driven strategy to estimate handwriting quality. Scientific Reports, 10(1), Article 3140. https://doi.org/10.1038/s41598-020-60011-8

• Biotteau, M., Danna, J., Baudou, É., Puyjarinet, F., Velay, J. L., Albaret, J. M., & Chaix, Y. (2019). Developmental coordination disorder and dysgraphia: Signs and symptoms, diagnosis, and rehabilitation. Neuropsychiatric Disease and Treatment, 15, 1873–1885. https://doi.org/10.2147/NDT.S120514

• Chai, J., Zeng, H., Li, A., & Ngai, E. W. T. (2021). Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, 6, Article 100134. https://doi.org/10.1016/j.mlwa.2021.100134

• Chung, P. J., Patel, D. R., & Nizami, I. (2020). Disorder of written expression and dysgraphia: Definition, diagnosis, and management. Translational Pediatrics, 9(Suppl 1), S46–S54. https://doi.org/10.21037/TP.2019.11.01

• Dankovicova, Z., Hurtuk, J., & Fecilak, P. (2019, September 12-14). Evaluation of digitalized handwriting for dysgraphia detection using random forest classification method. [Paper presentation]. IEEE 17th International Symposium on Intelligent Systems and Informatics, Proceedings (SISY), Subotica, Serbia. https://doi.org/10.1109/SISY47553.2019.9111567

• Deuel, R. K. (1995). Developmental dysgraphia and motor skills disorders. Journal of Child Neurology, 10(1_suppl), S6-S8. https://doi.org/10.1177/08830738950100S103

• Devi, A., & Kavya, G. (2023). Dysgraphia disorder forecasting and classification technique using intelligent deep learning approaches. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 120, Article 110647. https://doi.org/10.1016/j.pnpbp.2022.110647

• Devillaine, L., Lambert, R., Boutet, J., Aloui, S., Brault, V., Jolly, C., & Labyt, E. (2021). Analysis of graphomotor tests with machine learning algorithms for an early and universal pre-diagnosis of dysgraphia. Sensors, 21(21), Article 7026. https://doi.org/10.3390/s21217026

• Dimauro, G., Bevilacqua, V., Colizzi, L., & Di Pierro, D. (2020). TestGraphia, a software system for the early diagnosis of dysgraphia. IEEE Access, 8, 19564–19575. https://doi.org/10.1109/ACCESS.2020.2968367

• Ghouse, F., Paranjothi, K., & Vaithiyanathan, R. (2022). Dysgraphia classification based on the non-discrimination regularization in rotational region convolutional neural network. International Journal of Intelligent Engineering and Systems, 15(1), 55–63. https://doi.org/10.22266/IJIES2022.0228.06

• Kunhoth, J., Maadeed, S. A., Saleh, M., & Akbari, Y. (2023). Biomedical signal processing and control exploration and analysis of on-surface and in-air handwriting attributes to improve dysgraphia disorder diagnosis in children based on machine learning methods. Biomedical Signal Processing and Control, 83, Article 104715. https://doi.org/10.1016/j.bspc.2023.104715

• Masood, F., Khan, W. U., Ullah, K., Khan, A., Alghamedy, F. H., & Aljuaid, H. (2023). A hybrid CNN-LSTM random forest model for dysgraphia classification from hand-written characters with uniform/normal distribution. Applied Sciences, 13(7), Article 4275. https://doi.org/10.3390/app13074275

• Mohammed, A. B., Al-Mafrji, A. A. M., Yassen, M. S., & Sabry, A. H. (2022). Developing plastic recycling classifier by deep learning and directed acyclic graph residual network. Eastern-European Journal of Enterprise Technologies, 2(10), 42–49. https://doi.org/10.15587/1729-4061.2022.254285

• Qiao, J., Lv, Y., Cao, C., Wang, Z., & Li, A. (2018). Multivariate deep learning classification of alzheimer’s disease based on hierarchical partner matching independent component analysis. Frontiers in Aging Neuroscience, 10, Article 417. https://doi.org/10.3389/fnagi.2018.00417

• Ramlan, S. A., Isa, I. S., Osman, M. K., Ismail, A. P., & Soh, Z. H. C. (2022). Investigating the impact of CNN layers on dysgraphia handwriting image classification performance. Journal of Electrical and Electronic Systems Research, 21, 73–83. https://doi.org/https://doi.org/10.24191/jeesr.v21i1.010

• Rosli, M. S. A., Isa, I. S., Ramlan, S. A., Sulaiman, S. N., & Maruzuki, M. I. F. (2021, August 27-28). Development of CNN transfer learning for dyslexia handwriting recognition. [Paper presentation]. 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia. https://doi.org/10.1109/iccsce52189.2021.9530971

• Šafárová, K., Mekyska, J., & Zvončák, V. (2021). Developmental dysgraphia: A new approach to diagnosis. The International Journal of Assessment and Evaluation, 28(1), 143–160. https://doi.org/10.18848/2327-7920/CGP/v28i01/143-160

• Sihwi, S. W., Fikri, K., & Aziz, A. (2019). Dysgraphia identification from handwriting with support vector machine method. Journal of Physics: Conference Series, 1201(1), Article 012050. https://doi.org/10.1088/1742-6596/1201/1/012050

• Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002

• Vilasini, V., Rekha, B. B., Sandeep, V., & Venkatesh, V. C. (2022, August 11-12). Deep learning techniques to detect learning disabilities among children using handwriting. [Paper presentation]. Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), Kannur, India. https://doi.org/10.1109/ICICICT54557.2022.9917890

• Vaivre-Douret, L., Lopez, C., Dutruel, A., & Vaivre, S. (2021). Phenotyping features in the genesis of pre-scriptural gestures in children to assess handwriting developmental levels. Scientific Reports, 11(1), Article 731. https://doi.org/10.1038/s41598-020-79315-w

• Vlachos, F., & Avramidis, E. (2020). The difference between developmental dyslexia and dysgraphia: Recent neurobiological evidence. International Journal of Neuroscience and Behavioral Science, 8(1), 1–5. https://doi.org/10.13189/ijnbs.2020.080101

• Zolna, K., Asselborn, T., Jolly, C., Casteran, L., Johal, W., & Dillenbourg, P. (2019). The dynamics of handwriting improves the automated diagnosis of dysgraphia. arXiv:1906.07576, Article 1906.07576. https://doi.org/10.48550/arXiv.1906.07576