Data Science Project | Part 3 | Signature Recognitaion
Welcome to Part 3 of our Data Science Project series!
In this session, we delve into the fascinating field of signature recognition using machine learning techniques. Signature recognition plays a crucial role in authentication and fraud detection, making it a vital area of study in data science.
What You'll Learn:
Introduction to Signature Recognition: Understand the importance of signature recognition in security and financial transactions.
Overview of the Dataset: Explore the dataset used for signature recognition, including the different classes (genuine vs. forged signatures) and how to preprocess the data for model training.
Feature Extraction: Techniques for extracting meaningful features from signature images, such as shape analysis, texture features, and deep learning-based feature extraction methods.
Building Machine Learning Models: Step-by-step guide on building machine learning models for signature recognition, including SVMs (Support Vector Machines), Random Forests, and deep learning models like CNNs (Convolutional Neural Networks).
Model Evaluation: Understand how to evaluate the performance of your signature recognition models using metrics like accuracy, precision, recall, and F1 score.
Why Watch This session ?
This session is essential for anyone interested in understanding the principles and techniques behind signature recognition. By the end of this session, you'll have the knowledge to preprocess signature data, build effective recognition models, and evaluate their performance.
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