A Novel Approach for Hand-Written Digit Classification Using Deep Learning


  • Tamanna Sachdeva Assistant Professor Department of Computer Science and Technology ,Manav Rachna University Faridabad, 121003 Haryana, India
  • Nisha Jha Assistant Professor, Department of Information Technology, Jagannath International Management School, Vasant Kunj, New Delhi-110070, India.
  • Geeta Scholar of CSE, Northcap University, Sector 23 Gurugram,India.
  • Hemalatha Thanganadar Department of Health Informatics, College of Public Health and Tropical Medicine, Jazan University, Jazan, 45142, Kingdom of Saudi Arabia.
  • Mostafa M. Salah Assistant Professor Electrical Engineering Department, Future University in Egypt, Cairo 11835, Egypt
  • Chaman Kumar Assistant professor iimt college of engineering Greater Noida


Handwriting recognition, Support Vector Machine, Multi- Layer Perceptions, CNN


Humans' control over technology is at an all-time high, with applications ranging from visual object recognition to the dubbing of dialogue into silent films. Using algorithms for deep learning and machine learning. Similarly, the most crucial technologies are text line recognition fields of study and development, with an increasing number of potential outcomes. Handwriting recognition (HWR), also identified as Handwriting Text Acknowledgment, is the capacity of a computer to understand legibly handwritten input from bases such as paper documents, screens, and other devices. Evidently, we have performed handwritten digit recognition using MNIST datasets and SVM, Multi-Layer Perceptron (MLP), and CNN models in this research. Our primary purpose is to compare the accuracy and execution times of the aforementioned models to determine the optimal model for digit recognition.


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How to Cite

Sachdeva, T. ., Jha, N. ., Geeta , G. ., Thanganadar, H. ., Salah, M. M. ., Deep, S. ., & Kumar, C. . (2023). A Novel Approach for Hand-Written Digit Classification Using Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 301–311. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4253



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