Handwritten Digit and Signature Recognition using Deep Learning
Trains a CNN on the MNIST dataset and a custom signature corpus to recognise handwritten digits and verify signature authenticity for document-processing workflows.
- Project code
- AUX-MLR-1901
- Domain
- Machine Learning
- Type
- Software project
- Level · Year
- Beginner · 2025-26
Technologies and components
What is included
- Complete source code
- Database / dataset and setup guide
- Project report (college format)
- PPT for review and viva
- Installation and demo support
- Viva question guidance
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View project →Frequently asked questions
Can I get the Handwritten Digit and Signature Recognition using Deep Learning project for my final year?
Yes. This project (code AUX-MLR-1901) is available as a software project kit for the 2025-26 academic year. Contact us on WhatsApp to request it.
Can this project be customised?
Yes. Features, technology and documentation can be modified to suit your guide and college requirements.
Which departments is this project suitable for?
CSE, IT, AI & DS, BCA and MCA students, and ECE students interested in software.
Get this project with full support
Ask about customisation, delivery time and viva support.
