This project focuses on Handwritten Digits Recognition using machine learning techniques. The team utilized technologies such as Python, TensorFlow, and machine learning algorithms like Neural Networks and Convolutional Neural Networks (CNN).
- Operating Systems: Windows
- IDE: Jupyter/ Spyder/ Colab
- Programming Language: Python
The project aims to recognize handwritten digits (0 to 9) from the MNIST dataset. It includes a detailed exploration of the dataset, visualization of samples, and the implementation of two main models: General Neural Network and Convolutional Neural Network (CNN). The results include accuracy metrics and the ability to predict handwritten digits.
The team started by exploring the MNIST dataset, preprocessed the data, and visualized samples. Two main models, General Neural Network and CNN, were implemented, with a focus on optimizing accuracy. Results were obtained, and the models were evaluated. The project follows a systematic approach, emphasizing the importance of model selection and parameters.
The project provided hands-on experience in working with machine learning models, dataset exploration, and implementing neural networks. The team gained insights into model evaluation, accuracy metrics, and the significance of preprocessing in image recognition tasks.
Future enhancements could include extending the recognition system to alphabets or multiple languages. Additionally, the team suggests introducing new data sources for analysis to further improve the model's capabilities.
This project report highlights the journey from dataset exploration to model implementation, providing a comprehensive overview of the Handwritten Digits Recognition system using machine learning.