The main goal of the project is to develop a system that can recognize handwritten digits from images using a combination of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). In many real-world use cases such as processing handwritten forms, detecting digits on scanned documents, or automating postal code recognition manual digit entry is both time-consuming and error-prone. This project addresses that problem by training a robust digit classification model using image data. CNNs are used to extract image features and recognize spatial hierarchies, while SVMs further refine classification performance. By the end of the project, students will deliver a working system capable of predicting digits from visual input, laying a foundation for real-world automation solutions.
The twelve-week project begins with learning the fundamentals of CNNs and SVMs, followed by setting up the development environment using tools such as Python, Anaconda, or Google Colab. Early stages include data collection or acquisition (such as MNIST dataset), cleaning, and preparing labeled images of digits.
In the middle phase, students will build the CNN-SVM model architecture, train it on labeled datasets, and evaluate its accuracy using unseen data. Techniques like image augmentation, hyperparameter tuning, and cross-validation will be employed to improve results. Later stages will involve full integration of the model with a user interface, system testing, and performance optimization. Final deliverables will include the model, a complete documentation report, and a team presentation. While the system is designed for accuracy, the model may still be affected by variations in handwriting style or image quality.
