https://github.com/abhisek-13/handwritten_digit_classifier
This repository contains a CNN model for handwritten digit recognition with 98% accuracy. Implemented in a Jupyter Notebook, the code is easy to understand, modify, and use for further development, including creating your webpage.
https://github.com/abhisek-13/handwritten_digit_classifier
cnn deep-learning kaggle keras mnist-dataset pandas python tensorflow
Last synced: 3 months ago
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This repository contains a CNN model for handwritten digit recognition with 98% accuracy. Implemented in a Jupyter Notebook, the code is easy to understand, modify, and use for further development, including creating your webpage.
- Host: GitHub
- URL: https://github.com/abhisek-13/handwritten_digit_classifier
- Owner: abhisek-13
- Created: 2023-12-29T10:48:29.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-01T15:00:59.000Z (about 2 years ago)
- Last Synced: 2025-10-11T00:04:37.561Z (9 months ago)
- Topics: cnn, deep-learning, kaggle, keras, mnist-dataset, pandas, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 12.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Handwritten Digit Prediction Model
This repository contains a Convolutional Neural Network (CNN) based model for handwritten digit recognition with 98% accuracy. The project is implemented in a Jupyter Notebook (`.ipynb` file), and the code is designed to be easy to understand, modify, and use for further development, including creating your webpage using this model.
## Table of Contents
- [Overview](#overview)
- [Dataset](#dataset)
- [Model Architecture](#model-architecture)
- [Installation](#installation)
- [Usage](#usage)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)
## Overview
This project aims to classify handwritten digits (0-9) using a Convolutional Neural Network (CNN). The model is trained on the MNIST dataset and achieves an accuracy of 98%. The code is provided in a single Jupyter Notebook, making it easy to follow and adapt.
## Dataset
The model is trained on the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), which contains 60,000 training images and 10,000 test images of handwritten digits.
## Model Architecture
The CNN model used in this project consists of the following layers:
1. Convolutional Layer
2. Pooling Layer
3. Dropout Layer
4. Dense Layer
The model is built using the TensorFlow and Keras libraries.
## Installation
You need to have Python and Jupyter Notebook installed to run the code. You can install the required libraries using the following command:
```bash
pip install -r requirements.txt
```
The requirements.txt file should include the following dependencies:
- numpy
- pandas
- matplotlib
- tensorflow
- keras
## Usage
1. Clone the repository:
```bash
git clone https://github.com/abhisek-13/HandWritten_Digit_Classifier.git
cd HandWritten_Digit_Classifier
```
2. Open the Hand_Written_digit_Recognition.ipynb file and run all the cells to train the model and see the results.
## Results
The model achieves an accuracy of 98% on the MNIST test dataset. Below is a sample of predictions made by the model:
## Contributing
Contributions are welcome! Feel free to fork the repository and submit a pull request if you have any ideas or improvements. Please ensure your changes are well-documented and tested.
## License
This project is licensed under the MIT License. See the LICENSE file for more details.