Handwritten Digit Recognition

Project Domain / Category:

Image Processing

Abstract:

This project aims to develop a handwritten digit recognition system using deep learning techniques. The system will be trained on the image dataset, which contains images of handwritten digits (0-9). A Convolutional Neural Network (CNN) model will be implemented to classify the digits with high accuracy. The model will be integrated with a user-friendly interface that allows users to input digit images for real-time recognition. The system can be utilized in various real-world applications such as automated form processing, bank check verification, and postal code recognition.

Functional Requirements:

1.      Data Collection & Preprocessing:

o    Utilize the dataset for training and testing.

o    Apply image preprocessing techniques such as resizing, normalization, and noise reduction.

2.      Model Development:

o    Implement a Convolutional Neural Network (CNN) for digit classification.

o    Train the model using TensorFlow/Keras to achieve high accuracy.

o    Optimize the model using techniques like dropout and batch normalization.

3.      User Interface Development:

o    For Desktop (Tkinter/PyQt):

§   Provide an interface where users can draw or upload digit images.

§   Display the predicted digit along with the model's confidence score.

o    For Web (Flask/Django):

§   Develop a web-based interface to allow users to upload images for recognition.

4.      Real-Time Recognition:

o    Capture handwritten digits using a webcam or touch input.

o    Process and classify the digit in real-time using the trained model.

o    Display the recognized digit along with its probability score.

5.      Database Integration:

o    Store user-inputted digit images along with predictions for further analysis.

o    Maintain logs of recognition results for performance tracking.

6.      Performance Evaluation & Optimization:

o    Evaluate model accuracy using metrics such as precision, recall, and F1-score.

o    Improve accuracy through hyperparameter tuning and data augmentation.