Kunal Saini

Duration: July 2026 - Aug 2026

Short Project Description – Handwritten Digit Recognition Handwritten Digit Recognition is an AI/ML-based project developed to automatically recognize and classify handwritten digits from images. The system uses Python, image processing, and machine learning/deep learning techniques to analyze handwritten input and predict the corresponding digit from 0 to 9. The project demonstrates practical applications of computer vision, data preprocessing, model training, and image classification. Kunal's Role During Internship Collecting and preparing handwritten digit datasets Performing image preprocessing and normalization Analyzing and visualizing the dataset Implementing machine learning/deep learning models Training and evaluating the classification model Performing feature extraction and model optimization Testing the model with new handwritten digit images Measuring model performance using evaluation metrics Visualizing prediction results Debugging and improving model accuracy Documenting the methodology and results Managing project files using Git/GitHub Suggested Project Modules for the Internship Report Phase 1: Requirement Analysis & Problem Definition Phase 2: Dataset Collection & Understanding Phase 3: Image Preprocessing & Data Normalization Phase 4: Exploratory Data Analysis & Visualization Phase 5: Model Development & Training Phase 6: Model Evaluation & Performance Analysis Phase 7: Handwritten Digit Prediction Phase 8: Model Optimization & Testing Phase 9: Result Analysis & Documentation Phase 10: Final Testing & Deployment