https://github.com/aleksavukadinovic/digiteye
A Python and TensorFlow-powered neural network for recognizing handwritten digits with precision.
https://github.com/aleksavukadinovic/digiteye
Last synced: 4 months ago
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A Python and TensorFlow-powered neural network for recognizing handwritten digits with precision.
- Host: GitHub
- URL: https://github.com/aleksavukadinovic/digiteye
- Owner: AleksaVukadinovic
- Created: 2024-11-21T20:13:50.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-11-28T10:24:08.000Z (6 months ago)
- Last Synced: 2024-12-02T06:34:47.822Z (6 months ago)
- Language: Python
- Size: 4.24 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# DigitEye
This project implements a neural network using TensorFlow and Keras to recognize handwritten digits from the MNIST dataset. The model can also predict custom handwritten digit images.
---
## Features
- **Customizable Neural Network:** Dynamically set the number of layers, neurons, activation functions, and epochs.
- **Data Augmentation:** Increases the training dataset using techniques like rotation, shifting, and zooming to improve generalization.
- **Custom Predictions:** Load your custom digit images and let the model predict them.
- **Model Saving and Loading:** Train a new model or load an existing one.
---
## Installation
### Prerequisites
- Python 3.x
- TensorFlow
- NumPy
- OpenCV
- Matplotlib
Install the required libraries with:
```bash
pip install tensorflow numpy opencv-python matplotlib
```
Note for Linux users:
It is recommended to create a virtual environment to install these libraries, as direct pip installation may lead to system-wide conflicts. Use the following commands to set up a virtual environment:
```bash
python3 -m venv digit_eye_env
source digit_eye_env/bin/activate
pip install tensorflow numpy opencv-python matplotlib
```
## Usage
1. Clone the repository
```bash
git clone https://github.com/AleksaVukadinovic/DigitEye
cd DigitEye
```
2. Run the script
```bash
python digit_recognition.py
```
3. Follow the console prompts
## Materials Used
This project draws inspiration from and builds upon the following resources:
- [DeepLearning Series by 3Blue1Brown](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
An excellent visual introduction to the concepts behind neural networks and deep learning.
- [Handwritten Digit Recognition by NeuralNine](https://youtu.be/bte8Er0QhDg?si=Nf-ChxRHdiWNV2M0)
A practical guide to implementing a digit recognition system.
- [Veštačka inteligencija (Artificial Intelligence)](https://poincare.matf.bg.ac.rs/~janicic//books/VI_B5.pdf)
A comprehensive textbook by Mladen Nikolić and Predrag Janičić, providing theoretical insights into artificial intelligence.
- [Using Neural Networks to Recognize Digits](http://neuralnetworksanddeeplearning.com/chap1.html)
A detailed article explaining the mathematics and algorithms behind digit recognition using neural networks.