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.
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