akashkathole7 / hand_written_recognizer Goto Github PK

Handwritten Recognition This project is a handwritten recognition system that uses machine learning algorithms to recognize handwritten text. The system is built using Python programming language and utilizes the scikit-learn machine learning library.

Dependencies To run this project, you need to have the following dependencies installed:

Python 3.6 or higher Scikit-learn Numpy Matplotlib Dataset The dataset used in this project is the MNIST dataset. It contains 60,000 training images and 10,000 testing images of handwritten digits from 0 to 9.

Preprocessing Before training the model, the images are preprocessed to enhance their features. This includes converting the images to grayscale, resizing them to a uniform size, and normalizing their pixel values.

Training The model is trained using the scikit-learn library's support vector machine (SVM) classifier. The SVM classifier is a type of machine learning algorithm that is effective for classification tasks.

Testing To test the accuracy of the model, the testing dataset is used. The accuracy of the model is calculated by comparing the predicted labels with the actual labels of the testing images.

This will preprocess the data, train the model, and test the accuracy of the model. You can also modify the parameters of the SVM classifier to see how it affects the accuracy of the model.

Conclusion This project demonstrates the use of machine learning algorithms for handwritten recognition. The SVM classifier is an effective algorithm for this task and can achieve high accuracy with proper preprocessing and tuning of its parameters.