Currency Recognition is a format to identify what kind of currency and which country is that belongs to. PhD topics in Currency Recognition explains the ways and techniques which makes currency recognition process as automatic and robust. In the currency identification process, the trial results confirm that the precision recognition of paper currencies is more effective than coin currencies because the radiance create the salient points to a similar coin category is vary. So we calculate the accurate standard recognition method and the standard execution time for coin and paper currencies.

Importance of Currency Recognition

Generally, our world is filled various currencies. In each currencies, size of paper, colour, and pattern is varied. So it is tough for people to identify the symbols of all currencies correctly. So people decided to invent a powerful and apt system to recognizing a currency.

We continuously working in various country’s currencies recognition. This support is not only for any particular country and we some of currencies are follows: Dollars, Renminbi, Krone, Euro, Rupee, Rupieea, Dinar, Peso, Kroner, Yen, Rubles, Riyal, Franc, and Dirhams.

IMPORTANT FEATURES IN CURRENCY

Currently, there are more than 180 currencies are circulating all over the world. For example, here we mentioned few particular features in currency recognition.

• Hidden Image
• Amount printed area
• Intaglio Mark
• Identification Mark
• Transparent Printed Image
• Color Changing Link

    From those points, currencies are identified. Therefore, if hear it is like a simple task but actually not. Because of numerous different settings that might disturb the image quality. For rectifying those issues PhD topics in Currency Recognition have young talented engineers who are in the experiment to enhance that recognition of currencies to make accurate and much easier than before while using smartphones too.

In general, Note Sorting Machine is used in banks to identify currencies. Particularly for identifying currency device is named as Currency Sorting Machine. The major roles of Currency Sorting Machine are catching and detection of images. Technically, Currency Sorting Machines has a performance like,

• Amalgamation with Visual, Mechanical and automation.
• Calculation.
• Rapid Model Identification.

We can use Matlab, Python, OpenCV, Scilab, LabView for currency recognition. So you can use your research topic in currency recognition. In this field, image processing, computer vision and pattern recognition methods are used frequently. For instance, image processing operations such as artifacts removal, smoothing, and object detection are powerful and gives an exact result. But additionally, it has to keep more various characteristics and authentic marks for various frequently used currencies in banks.

How does currency recognition works?

• Obtain the image of the target currency using one of the possible methods (e.g. Camera, Scanner, etc.)
•  Use Image Pre-processing algorithms to change the nature of the image in order to extract     required information
• Detect the boundaries and extract the ROI (Region of Interest) using cropping
• Extract the desired features
• Compare the extracted feature values with ideal feature values that are calculated.
• Display the outputs

5 Research Ideas in Currency Recognition

Currency recognition can be mainly utilized for 5 tasks

• Backnote region segmentation: Partition an image into sets of pixels. This sets of pixels may identify  the image objects.It can be achieved by 3 methods. 1. Corner detection 2. Least square method 3. Segmentation based on component labeling.
• Noise removal and gray level reduction:  It checks the consistent level of the pixel to identify and remove the noise. Weiner, median and gray level filtering are used in this task.
• Normalize the brightness and contrast enhancement: It is  the process of altering the  pixel intensity values.Histogram equalization is the one of the most used technique for this kind of normalization.
• Image resolution reduction:  It is the reduction of number of pixels displayed per inch of an image which can be done by nearest neighbor interpolation
• Image channel reduction : Channel reduction is carried out by the selection of spectral information from spatial information. It can be reduced by converting the color to gray.

Obviously, due to further research, there is a big scope of auxiliary improvement in terms of efficiency and accuracy of Coins and banknotes recognitions system under PhD topics in Currency Recognition. If you interested to bring revolution in this currency recognition python techniques without hesitation mingle with us. To be very sure we guide you with our full support of our experts to engraving your name in your department. Let’s shine in your field of study with us!!