Abstract
What Digit You Write is an open-source AI & Machine Learning project. 🎰Handwritten digit recognition application implemented by TensorFlow2 + Keras and Flask. It is built using Python, Flask. The complete source code is publicly available on GitHub under the Do What The F*ck You Want To Public License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
Handwritten digit recognition application implemented by TensorFlow2 + Keras and Flask.
If the clone is too slow, you can use the following method
2. Objective
🎰Handwritten digit recognition application implemented by TensorFlow2 + Keras and Flask.
This project demonstrates how Python, Flask can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/InnoFang/what-digit-you-write.git
cd what-digit-you-write$ git clone --depth 1 https://github.com/InnoFang/what-digit-you-write.git
$ cd what-digit-you-write
$ conda create --name <env> --file requirements.txt
$ conda activate <env>
$ python app.py$ # git clone --depth 1 https://github.com.cnpmjs.org/InnoFang/what-digit-you-write.gitFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by InnoFang and published on GitHub under the Do What The F*ck You Want To Public License. Please follow the license terms and credit the original author when you use or modify this code.
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