Mnist dataset_Image Classification using Opencv.pptx

It is image classification project.

1. Weekly report Project- Image Classification using OpenCV

2. Dataset info: Dataset loaded using keras library namely mnist dataset. Problem Statement: This project aims to develop an accurate image classification system using OpenCV to categorize unseen images into predefined classes, potentially leveraging deep learning for improved performance. The challenge lies in balancing data acquisition, processing speed for real-time applications (if applicable), and achieving a desired level of classification accuracy.

3. Data Introduction: I t consists of a large collection of grayscale images of handwritten digits (0 through 9), each measuring 28 pixels by 28 pixels. Originally constructed from scanned documents, MNIST has become a standard dataset. Data Preprocessing: I employed OpenCV functionalities for tasks such as transforming the data type, reshaping and normalization to prepare the MNIST dataset for analysis.

4. Data Visualization I have also visualized some digits by using imshow function.

5. Model Training: Implemented machine learning models, leveraging OpenCV's integration with frameworks like Support vector classifier to classify handwritten digits with high accuracy. Evaluation and Results: Evaluated the performance of the trained models using appropriate metrics and approximate accuracy is 98 percent wit recall and precision of almost 99 percent and 98 percent.

6. Correctly classified class by model Incorrectly classified class by model There is total 10,000 classes data in testing phase and model predicted 9833 classes correctly.