{"id":24965908,"url":"https://github.com/abhisek-13/handwritten_digit_classifier","last_synced_at":"2026-04-15T05:31:16.829Z","repository":{"id":214615817,"uuid":"736948753","full_name":"abhisek-13/HandWritten_Digit_Classifier","owner":"abhisek-13","description":"This repository contains a CNN model for handwritten digit recognition with 98% accuracy. 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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.\n\n## Table of Contents\n\n- [Overview](#overview)\n- [Dataset](#dataset)\n- [Model Architecture](#model-architecture)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Results](#results)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Overview\n\nThis 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.\n\n## Dataset\n\nThe 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.\n\n## Model Architecture\n\nThe CNN model used in this project consists of the following layers:\n\n1. Convolutional Layer\n2. Pooling Layer\n3. Dropout Layer\n4. Dense Layer\n\nThe model is built using the TensorFlow and Keras libraries.\n\n## Installation\n\nYou need to have Python and Jupyter Notebook installed to run the code. You can install the required libraries using the following command:\n\n```bash\npip install -r requirements.txt\n```\nThe requirements.txt file should include the following dependencies:\n\n- numpy\n- pandas\n- matplotlib\n- tensorflow\n- keras\n## Usage\n1. Clone the repository:\n```bash\ngit clone https://github.com/abhisek-13/HandWritten_Digit_Classifier.git\ncd HandWritten_Digit_Classifier\n```\n2. Open the Hand_Written_digit_Recognition.ipynb file and run all the cells to train the model and see the results.\n\n## Results\nThe model achieves an accuracy of 98% on the MNIST test dataset. Below is a sample of predictions made by the model:\n\n## Contributing\nContributions 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.\n\n## License\nThis project is licensed under the MIT License. See the LICENSE file for more details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhisek-13%2Fhandwritten_digit_classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhisek-13%2Fhandwritten_digit_classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhisek-13%2Fhandwritten_digit_classifier/lists"}