FPGA-Based Deep Neural Network Implementation for Handwritten Digit Recognition
This paper presents a field programmable gate array (FPGA)–based implementation of a deep neural network (DNN) for handwritten digit recognition. We propose the use of a fully connected four-layer neural network with the hidden layers implementing the ReLU activation function and the output layer ba...
Saved in:
| Main Authors: | Matej Štajnbrikner, Igor Valek, Tomislav Matić, Mario Vranješ |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/am/8901861 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Investigation of Hardware Implementation of a Feedforward Neural Network for Handwritten Digit Recognition Based on FPGA
by: E. A. Krivalсevich, et al.
Published: (2025-04-01) -
Handwritten Geez Digit Recognition Using Deep Learning
by: Mukerem Ali Nur, et al.
Published: (2022-01-01) -
Similar handwritten Chinese character recognition based on deep neural networks with big data
by: Zhao YANG, et al.
Published: (2014-09-01) -
BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks
by: Abu Sufian, et al.
Published: (2022-06-01) -
Digits Recognition for Arabic Handwritten through Convolutional Neural Networks, Local Binary Patterns, and Histogram of Oriented Gradients
by: Bushra Mahdi Hasan, et al.
Published: (2024-10-01)