Project information
- ๐ฏ Objective:: The project focuses on creating a system capable of recognizing human handwritten digits from various sources such as images and touch screens, and classifying them into ten predefined classes (0-9). This technology has broad applications including number plate recognition and postal mail sorting.
- date: Dec 2022 - Dec 2022
- Github Link: Github Link
Description:
๐ก Features:
- ๐๐ฎ๐ฅ๐ญ๐ข๐ฉ๐ฅ๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐จ๐๐๐ฅ๐ฌ: Implements and compares multiple machine learning modelsโSupport Vector Machine (SVM), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN)โto determine the most effective approach for digit recognition.
- ๐๐จ๐ฆ๐ฉ๐ซ๐๐ก๐๐ง๐ฌ๐ข๐ฏ๐ ๐๐จ๐๐๐ฅ ๐๐ฏ๐๐ฅ๐ฎ๐๐ญ๐ข๐จ๐ง: Evaluates each model based on accuracy, error rates, and training-testing times, providing a detailed comparison of their performance.
- ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐๐ฌ๐ฎ๐ฅ๐ญ๐ฌ: Uses matplotlib to create plots and charts that visualize the performance metrics, aiding in the analysis and comparison of each modelโs effectiveness.
- ๐๐๐ ๐๐ซ๐๐ก๐ข๐ญ๐๐๐ญ๐ฎ๐ซ๐: The CNN model includes convolutional and pooling layers, suitable for grid-structured data like images, with dropout layers to prevent overfitting. The model is compiled using the Adadelta optimizer, optimizing its learning process.
- ๐๐๐๐๐ ๐๐๐ญ๐๐ฌ๐๐ญ: Utilizes the MNIST dataset, which is highly popular in the machine learning community, containing 60,000 training images and 10,000 test images of handwritten digits, represented in a 28ร28 pixel grayscale matrix.
These features underscore the projectโs capability to leverage advanced machine learning techniques to improve the accuracy and efficiency of handwritten digit recognition systems.