It is difficult for people to recognize currencies from different countries. Our aim is to help people solve this problem. However, currency recognition systems that are based on image analysis entirely are not sufficient.
Our system is based on image processing and makes the process automatic and robust. We use SEK and Chinese RMB as examples to illustrate the technique. Color and shape information are used in our algorithm.
Introduction:
There are approximately 50 currencies all over the world, with each of them looking totally different. For instance the size of the paper is different, the same as the colour and pattern. The staffs who work for the money exchanging (e.g. Forex Bank) have to distinguish different types of currencies and that is not an easy job. They have to remember the symbol of each currency.
This may cause some problems (e.g. wrong recognition), so they need an efficient and exact system to help their work. As we mentioned before, the aim of our system is to help people who need to recognize different currencies, and work with convenience and efficiency.
Currency Recognition System
For bank staffs, there is a “Currency Sorting Machine” helps them to recognize different kinds of currencies. The main working processes of “Currency Sorting Machine” are image acquisition and recognitions. It is a technique named “optical, mechanical and electronic integration”, integrated with calculation, pattern recognition (high speed image processing), currency anti-fake technology, and lots of multidisciplinary techniques.
It is accurate and highly-efficient. But for most staffs, they have to keep a lot of different characteristics and anti-fakes label for different commonly-used currencies in their mind. However, each of them has a handbook that about the characteristics and anti-fakes labels of some less commonly-used currencies. Even for that, no one can ever be 100 per cent confident about the manual recognition.
Otherwise our system is based on image processing, techniques which include filtering, edge detection, segmentation, etc. In order to make the system more comprehensive, we need to create a small database for storing the characteristics of the currency. In our system, we take Chinese RMB and Swedish SEK as examples. The system will be programmed based on MATLAB and include a user-friendly interface. The main steps in the system are:
1. Read image, reading the image we get from scanner as well as the format of the image is JPEG.
2. Pre-processing, removing noise, smoothening image.
3. Image process, edge detection, segmentation, pattern matching.
4. Results printing.
Basically the images are read from different derivations. However, we delimit our system which can read the currency from scanner. The device that the system needs is very common in our daily life, so we do not need to buy an extra device to realize the system.
There are some similar recognition systems, such as face recognition system, fingerprint recognition system. However the theories they use are similar but the techniques and approaches are different.
Authors: Yaojia Wang | Siyuan Lin
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