Handwritten Type Recognition Project

Overview

This project focuses on building machine learning models to recognize handwritten text and classify it into different types based on various features extracted from the handwriting.

Dataset

The dataset used in this project contains samples of handwritten text for each type of handwriting. Each sample is labeled with the corresponding class. The dataset is available in the file handwritten_data.csv.

Files

• pendigits_txt.csv: The dataset containing samples of handwritten text and their corresponding classes.
• Capstone Project-5 Student (Hand_Written_Digits)_results.ipynb: Jupyter Notebook containing the code for data analysis, exploratory data analysis (EDA), outlier analysis, visualization, and building machine learning models.

Data Analysis

• Exploratory data analysis (EDA) is performed to understand the structure and characteristics of the dataset.
• Outlier analysis is conducted to identify and handle outliers in the data.

Machine Learning Models

• Machine learning models are built using various classification algorithms.
• Hyperparameter tuning is performed to optimize the performance of the models.

Results

• The performance of the machine learning models is evaluated using appropriate classification metrics such as accuracy, precision, recall, F1-score, etc.
• The predictions of the models are compared with actual handwritten classes to assess their effectiveness.