Handwritten digit recognition is an important application of machine learning, particularly in the field of computer vision. The task involves identifying handwritten digits from an image and classifying them into the corresponding numerical values. In this project, we have developed a Flask-based application that recognizes handwritten digits using a pre-trained machine learning model.

Objective:

The objective of this project is to build a machine learning model that can accurately recognize handwritten digits and to develop a Flask-based web application that utilizes the model to recognize digits entered by users.

Methodology:

We used the MNIST dataset for training and testing our machine learning model. This dataset consists of 60,000 training images and 10,000 test images of handwritten digits from 0 to 9. We used a convolutional neural network (CNN) architecture to train our model on this dataset.

Once the model was trained and tested, we saved it as a serialized object using the joblib library. We then developed a Flask-based web application that allows users to draw a digit using their mouse or touchscreen and submit the image to the model for recognition.

Results:

Our machine learning model achieved an accuracy of 99.1% on the MNIST test set. When integrated with the Flask application, the model is able to accurately recognize handwritten digits drawn by users in real-time.

Technology Overview:

1. Machine Learning – Machine learning is a subfield of artificial intelligence that enables machines to learn from data, without being explicitly programmed. In this project, we used machine learning algorithms to recognize handwritten digits.
2. Convolutional Neural Networks – Convolutional Neural Networks (CNNs) are a class of deep neural networks commonly used in image processing and computer vision tasks. CNNs are designed to recognize visual patterns directly from pixel images, making them well-suited for tasks like image classification and object detection.
3. Flask – Flask is a lightweight web framework that enables the development of web applications in Python. In this project, we used Flask to develop a web application that allows users to input handwritten digits and receive predictions from the trained machine learning model.
4. MNIST Dataset – The MNIST dataset is a large database of handwritten digits commonly used for training and testing machine learning models. The dataset consists of 60,000 training images and 10,000 test images of handwritten digits from 0 to 9.
5. Joblib – Joblib is a library for Python that enables the efficient serialization and deserialization of Python objects. In this project, we used Joblib to save and load the trained machine learning model.

Technology Used in the project :-

1. We have developed this project using the below technology
2. HTML : Page layout has been designed in HTML
3. CSS : CSS has been used for all the desigining part
4. JavaScript : All the validation task and animations has been developed by JavaScript
5. Python : All the business logic has been implemented in Python
6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

1. We can configure this project on following operating system.
2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
3. Python 3.6.10, PIP, Django.
4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : –

1. python 3.6.8
2. command 1 – python -m pip install –user -r requirements.txt
3. command 2 – python app.py

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