Humans can see and visually sense the world around them by using their eyes and brains. Computer vision works on enabling computers to see and process images in the same way that human vision does. The goal of our work will be to create a model that will be able to identify and determine the handwritten digit from its image with better accuracy. We aim to complete this by using the concepts of Convolutional Neural Network and MNIST dataset. Though the goal is to create a model which can recognize the digits, we can extend it for letters and then a person’s handwriting. Through this work, we aim to learn and practically apply the concepts of Convolutional Neural Networks.
Bahramian, M., Azimzadeh Irani, A., Pourgholi, R., & Aliyari Boroujeni, A. (2024). Recognition of English Handwritten Digit using Convolutional Neural Network (CNN). Soft Computing Journal, (), -. doi: 10.22052/scj.2024.243236.1021
MLA
Mahsa Bahramian; Arash Azimzadeh Irani; Reza Pourgholi; Ahmad Aliyari Boroujeni. "Recognition of English Handwritten Digit using Convolutional Neural Network (CNN)". Soft Computing Journal, , , 2024, -. doi: 10.22052/scj.2024.243236.1021
HARVARD
Bahramian, M., Azimzadeh Irani, A., Pourgholi, R., Aliyari Boroujeni, A. (2024). 'Recognition of English Handwritten Digit using Convolutional Neural Network (CNN)', Soft Computing Journal, (), pp. -. doi: 10.22052/scj.2024.243236.1021
VANCOUVER
Bahramian, M., Azimzadeh Irani, A., Pourgholi, R., Aliyari Boroujeni, A. Recognition of English Handwritten Digit using Convolutional Neural Network (CNN). Soft Computing Journal, 2024; (): -. doi: 10.22052/scj.2024.243236.1021
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