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AI/ML Mini Project Source Code + Docs + PPT

Handwritten Digit Recognition Project

A Handwritten Digit Recognition project in Python using a CNN trained on MNIST to recognise digits 0-9 drawn by users.

  • Python
  • TensorFlow/Scikit-learn
  • PostgreSQL
  • Remote setup included
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See the project running first, then pay.

Handwritten Digit Recognition - Mathematics (cryptography) on a blackboard
Image: David Malone / CC BY 4.0 via Wikimedia Commons

What is the Handwritten Digit Recognition?

The Handwritten Digit Recognition project teaches a computer to read handwritten numbers from 0 to 9. It uses the famous MNIST dataset of 70,000 digit images to train a Convolutional Neural Network (CNN).

After training, the model reaches about 99% accuracy. Users can draw a digit on a canvas in the web app or upload an image, and the model predicts the digit instantly with a confidence score. It is a perfect first deep learning project, built with Python, TensorFlow/Keras and Flask.

Best for: BE / B.Tech (CSE, IT, AI & DS), MCA, MSc and BSc Computer Science students. Good as a mini project for your semester submission.

Key Features

Main things you can do with the Handwritten Digit Recognition.

  • MNIST dataset of 70,000 images
  • Image preprocessing and normalisation
  • CNN model with Keras
  • About 99% test accuracy
  • Draw a digit on canvas
  • Upload digit image
  • Prediction with confidence score
  • Accuracy and loss charts

Project Modules

The project is divided into these modules. Each one has its own screens and tasks.

1Data

  • Load MNIST
  • Normalise
  • Reshape
  • Train-test split

2Model

  • CNN layers
  • Training
  • Evaluation
  • Save model

3Web App

  • Drawing canvas
  • Image upload
  • Predict
  • Show confidence

4Analysis

  • Accuracy graph
  • Loss graph
  • Confusion matrix
  • Sample predictions

What You Get

Everything you need to submit, run and explain the project.

Complete Source Code

Full, working source code with clean folder structure and helpful comments. No locked or hidden files.

Database File

Ready-to-use PostgreSQL database with tables and sample data, so the project runs on day one.

Project Documentation

Full project report in Word and PDF: abstract, SRS, system architecture, dataset details, algorithm explanation, accuracy charts and confusion matrix, screenshots, testing and conclusion.

PPT Presentation

A 15–20 slide presentation for your seminar, review or final viva. Easy to edit with your name and college.

Remote Project Setup

We connect to your laptop with AnyDesk or Google Meet, install everything and run the project for you.

Project Explanation

A simple walkthrough of the code and flow, plus common viva questions, so you can explain it with confidence.

Project Documentation includes

  1. Certificate, acknowledgement and abstract
  2. Introduction, problem statement and objectives
  3. Existing system vs proposed system
  4. Software Requirement Specification (SRS)
  5. System design: system architecture, dataset details, algorithm explanation, accuracy charts and confusion matrix
  6. Module description and screenshots
  7. Testing and test cases
  8. Conclusion, future scope and references

PPT Presentation slides

  1. Title and team details
  2. Introduction and problem statement
  3. Objectives
  4. Existing vs proposed system
  5. System architecture
  6. Modules
  7. Technology used
  8. Screenshots / demo
  9. Advantages and future scope
  10. Conclusion

Tech Stack

PartTechnologyUsed for
LanguagePython 3Model training and app logic
ML LibrariesTensorFlow/Scikit-learnTraining and prediction
DataNumPy, Pandas, MatplotlibCleaning and charts
Web UIFlask / StreamlitSimple page to test the model
StoragePostgreSQLSaves users, inputs and results

Software and hardware requirements

  • Laptop with 8 GB RAM (GPU is optional, not required)
  • Python 3.9 – 3.11 and pip
  • VS Code or Jupyter Notebook
  • Libraries from requirements.txt (we include it)

How the Handwritten Digit Recognition Works

1

Dataset

A public dataset is loaded, cleaned and split into train and test parts.

2

Training

The model learns patterns from the training data.

3

Testing

Accuracy, precision and recall are checked on test data.

4

Prediction

Users give new input in the web page and get the result instantly.

Remote Project Setup Guidance

Not sure how to run it? We set up the project on your laptop over AnyDesk or Google Meet. No need to visit us.

  1. 1
    Share your details
    Tell us your laptop type (Windows / Mac) and a time that suits you.
  2. 2
    Install AnyDesk
    Install free AnyDesk (or join Google Meet) and share the access code.
  3. 3
    We set it up
    We install the required software, import the database and configure the machine learning project.
  4. 4
    Test together
    We run the project in front of you and check every main feature.
  5. 5
    Understand it
    We explain the code, flow and database so you are ready for your viva.

  1. Install Python 3 and tick Add Python to PATH during setup.
  2. Open the project folder in VS Code and create a virtual environment: python -m venv venv.
  3. Install the libraries: pip install -r requirements.txt.
  4. Run the notebook or training script once to train the model (a trained model is also included).
  5. Start the web app with python app.py and open localhost:5000 in your browser.

Handwritten Digit Recognition Images

Handwritten Digit Recognition - Pure-mathematics-formulæ-blackboard
Pure-mathematics-formulæ-blackboard Image: Wallpoper / Public domain via Wikimedia Commons
Handwritten Digit Recognition - Classroom scene in Washington, D.C. elementary school - children working with blocks and a
Classroom scene in Washington, D.C. elementary school - children working with blocks and a Image: Johnston, Frances Benjamin, 1864-1952, photographer / Public domain via Wikimedia Commons

What You Will Learn

Data cleaning and feature engineering Training and comparing ML / DL models Measuring accuracy, precision, recall and F1-score Saving a model and using it in a Flask app Explaining your results in the viva

Frequently Asked Questions

Yes. Handwritten Digit Recognition is a practical mini project that solves a real problem. It is suitable for BE / B.Tech (CSE, IT, AI & DS), MCA, MSc and BSc Computer Science students. It is easy to explain in a viva and easy to extend with your own ideas.

This machine learning project is built with Python, TensorFlow/Scikit-learn, PostgreSQL. The full technology list is in the "Tech Stack" section above.

Yes. You get the complete source code and the PostgreSQL database file with sample data. Nothing is locked or hidden.

Yes. You get a full project report (Word and PDF) with diagrams and screenshots, and a ready PPT presentation for your review or viva.

Yes. We offer remote setup using AnyDesk or Google Meet. We install the software, run the project and explain how it works.

Yes. Small changes such as your college name, colours or extra fields can be done. Bigger custom features can be discussed on call or WhatsApp.

We follow a simple rule: you see the project running first, then you make the payment. You can pay by UPI or bank transfer.

Project Details

Available
  • ProjectHandwritten Digit Recognition
  • TypeMini Project
  • CategoryAI/ML
  • LanguagePython
  • FrameworkTensorFlow/Scikit-learn
  • DatabasePostgreSQL
  • DocumentationReport + PPT
  • SetupRemote help
  • DeliverySource Code + Documentation
Chat on WhatsApp
Get the project first, then pay. No advance needed.

Need help? Call or WhatsApp

+91 90670 53826 +91 91451 51367

Available every day, 9 AM – 9 PM IST.

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