Machine Vision Based System for the Recognition and Counting of Indian Coins

Counting coins, with speed and accuracy, has been a challenging issue for banks and stores. People used to count coins manually before the arrival of coin counting machines. The process of counting coins manually is a very time consuming and tedious job. Moreover, mistakes are likely to occur due to various reasons such as fatigue, eye tiredness and too many coins of nearly same shape and size cause confusion in sorting and counting. Coin sorters are common in North America and can be found in most commercial banks and even some grocery stores. By contrast, they are not available in India, where the number and similarity of the coins make for a very challenging problem. The objective of this project is to determine whether advanced machine vision techniques are able to sort coins from India with acceptable speed and accuracy. If the answer is yes, then the outcome will be used to develop a machine that can recognize and count Indian coins, with Indian banks as the initial market.

Faculty Supervisor:

Brian Surgenor

Student:

Keyur Joshi

Partner:

9293507 Canada Inc.

Discipline:

Engineering - mechanical

Sector:

Finance, insurance and business

University:

Queen's University