This project focuses on detecting handwritten digits using machine learning techniques. It includes preprocessing the MNIST dataset, dimensionality reduction, training models such as Regression and Neural Networks, and deploying a functionality to recognize digits on a screen board and Vocalize the recognized digits.
- Python
- Jupyter Notebook
- Numpy, Pandas
- TensorFlow, Keras
- Matplotlib
- OpenCV
- pygame
- pyttsx3
- Data Preprocessing: Includes normalization, reshaping, and visualization of the MNIST dataset.
- Dimensionality Reduction: Used to reduce feature dimensions for better visualization.
- Model Training: Implements Logistic Regression and Neural Networks for digit classification.
- Digit Recognition on Screen Board: Utilizes OpenCV for real-time digit recognition on a screen board, with audio output.
- MNIST Dataset: Preprocessed and used for training and testing.
- Model Evaluation: Metrics such as accuracy, confusion matrix, and classification reports are provided.
- Real-time Digit Recognition: Ability to detect and audibly announce handwritten digits on a screen board.
Result Digit Board -
Accuracy Score -
Dataset image -
Model Summary -
The project utilizes the MNIST dataset, which is included in many machine learning libraries or can be downloaded from MNIST.
- Anshul Rathee
This project is licensed under the Apache License 2.0.