AI Based System for Recognizing

Handwritten (Graffiti) Numerals

CMIS 4114 – Artificial Intelligence

Mini Project – Group No. 06

Group Members

• 112212 – R. A. N. C. Ranasinghe• 112217 – W. M. B. Nayanadarshani

Problem Definition• Digital Character Recognition is one of the low-end

applications of the AI.• Since, the handwritings are different from person-to-

person, simple scanning or pixel based identification is not very effective.

• Therefore, such systems can be designed / implemented by training an AI agent to identify graffiti numerals.

• A graffiti inserted through a mouse or touch device has to be identified without depending on the persons’ unique writing characteristics.

Technologies

• Microsoft Visual Studio IDE• Visual Basic

Neural Networks

• A Neural Network, consisted of 03 neuron layers is implemented to achieve the goal.

• In the Network, layers are connected following a Learning Vector Quantization (LVQ) algorithm and Self-Organizing Maps operate hand-to-hand to analyze, identify and to deliver the final result.

AI Algorithms• Learning Vector Quantization (LVQ)

This is a supervised learning system where the training process is defined by the user. The system does not have a topological structure.

Demonstration

Pros & Cons Pros Cons

User-friendly GUI The System struggles and sometimes give false outputs to the similar digits (1 and 7, 3 and 5, 8 and 9).

User-definable training process

The System fails to self-learn the patterns.

Relatively simple operation

No feedback database is maintained for tried out patterns.

Small, compact size. The necessity of intensive training

The input matrix is expandible

The mouse is not a flexible input device

Thank You. . !