A Convolutional Neural Network for Handwritten Digit Recognition


  • Cristina Guevara Neri Universidad Autónoma de Ciudad Juárez, Instituto de Ingeniería y Tecnología, Doctorado en Ciencias en Ingeniería (DOCIA)
  • Osslan Osiris Vergara Villegas
  • Vianey Guadalupe Cruz Sánchez
  • Manuel Nandayapa
  • Juan Humberto Sossa Azuela


CNN, Digit Recognition, Image Preprocessing


Technological development in recent years has generated the constant need to digitalize and analyze data, where handwritten digit recognition is a popular problem. This paper focuses on the creation of two handwritten digit datasets and their use to train a Convolutional Neural Network (CNN) to classify them, also, a proposed extra preprocessing technique is applied to the images of one of the data sets. Experiments show that the proposed preprocessing technique lead to obtain accuracies above 98%, which were higher than the values obtained with the dataset without the additional preprocessing.




How to Cite

Guevara Neri, C., Vergara Villegas, O. O., Cruz Sánchez, V. G., Nandayapa, M., & Sossa Azuela, J. H. (2020). A Convolutional Neural Network for Handwritten Digit Recognition. International Journal of Combinatorial Optimization Problems and Informatics, 11(1), 97–105. Retrieved from https://ijcopi.org/ojs/article/view/163




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