Phase-1 Submission
Template Student Names: sudarsan.k
Register Number: [422223149027]
Institution: [surya group of institutions]
Department: [computer science engineering]
Date of Submission: [22/04/2025]
1. Problem Statement
The task of recognizing handwritten digits is a significant challenge in the field of
computer vision and machine learning.
Handwritten digit recognition is crucial for automating processes in various sectors,
such as banking (check processing), postal services (address recognition), and
education (grading).
This problem is important as it enhances efficiency, reduces human error, and enables
the development of smarter AI applications that can interpret human input more
accurately.
2. Objectives of the Project
Develop a deep learning model capable of accurately recognizing handwritten digits
from images.
Achieve a high accuracy rate (e.g., above 95%) on the MNIST dataset.
Generate insights into the performance of different neural network architectures (e.g.,
CNNs, RNNs).
Create a user-friendly application that can take handwritten input and provide real-time
digit recognition.
3. Scope of the Project
Features to analyze:
● Image preprocessing techniques (normalization, resizing).
● Different neural network architectures (Convolutional Neural Networks, Fully
Connected Networks).
● Performance evaluation metrics (accuracy, precision, recall).
Limitations:
● The project will focus on the MNIST dataset, which contains digits from 0 to 9.
● The model may not generalize well to handwritten digits outside the MNIST
dataset without further training.
4. Data Sources
Dataset: MNIST Handwritten Digits Dataset
● Source: Available publicly on platforms like Kaggle and the official MNIST
website.
● Type: Public dataset.
● Nature: Static dataset (downloaded once).
5. High-Level Methodology
Data Collection
● Download the MNIST dataset from a public repository.
Data Cleaning
● Ensure images are in a consistent format (28x28 pixels, grayscale).
● Check for any corrupted images and remove them if necessary.
Exploratory Data Analysis (EDA)
● Visualize sample images of each digit.
● Analyze the distribution of digits in the dataset.
Feature Engineering
● Normalize pixel values to a range of 0 to 1 to improve model performance.
● Augment the dataset with techniques like rotation and scaling to enhance
robustness.
Model Building
● Experiment with various architectures, primarily Convolutional Neural Networks
(CNNs), due to their effectiveness in image recognition tasks.
Model Evaluation
● Use metrics such as accuracy, confusion matrix, and F1 score to evaluate model
performance.
● Implement cross-validation to ensure the model's reliability.
Visualization & Interpretation
● Present results using confusion matrices and accuracy graphs.
● Create dashboards to visualize model performance and predictions.
Deployment
● Deploy the model as a web application using frameworks like Flask or Streamlit,
allowing users to input handwritten digits for recognition.
6. Tools and Technologies
Programming Language
● Python
Notebook/IDE
● Jupyter Notebook or Google Colab
Libraries
● TensorFlow/Keras for building and training the model.
● NumPy and Pandas for data manipulation.
● Matplotlib and Seaborn for data visualization.
Optional Tools for Deployment
Flask or Streamlit for creating the web application interface.
7. Team Members and Roles
Team Member 1: Data Scientist
● Responsible for data collection, cleaning, and exploratory data analysis.
Team Member 2: Machine Learning Engineer
● Focused on model building, evaluation, and optimization.
Team Member 3: Software Developer
● In charge of deploying the application and ensuring user interface functionality.
Team Member 4: Project Manager
● Oversees project timelines, coordination among team members, and
documentation.
