The main objective of currency recognition is to detect and extract the identified to drive invisible marks of a paper for better categorization. Nowadays due to the growth of world economics, currency recognition plays a cardinal role in trade, which is clearly described in this Assignment help in currency recognition. There are across 200 divergent types of currencies used by various countries throughout the world.
Why currency recognition is important?
Human can’t able to remember the symbol of different types of currencies. Hence we need an unfailing system to do this. World economy faces a major threat on Currency duplication also called counterfeit currency because of technology rise in printing and scanning. Recently Bangladesh economy is highly suffered by currency duplication.
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Important Steps in Currency Recognition
Image processing techniques are highly utilized for currency recognition that has four fundamental steps,
- Reading of image through scanner
- Noise removal and image smoothening
- Processing of Image via edge detection, segmentation, pattern matching
- Visualization of recognized results
TYPES OF CURRENCY RECOGNITION
The term currency recognition has been achieved by two types which are given below,
- It scans the entire paper
- Specify the correct direction and indicate the position
- Applied in Banking system and places which include money counters
- It focus on any part of the paper
- Specify the correct direction is not important , managed by any direction
- Worked on the basis of visual Bag of Words (BoW) method and noisy images are managed in a better way
Digital Image Processing in Currency Recognition
With the growth of modern banking services, automatic methods for paper currency recognition become vital in many areas such as vending machines. In this case, Digital Image Processing involves changing the nature of an image in order to improve its pictorial information for human interpretation. A number of methods for banknote classification have been proposed.
Currency Recognition task is achieved by many techniques. In this assignment help in currency recognition, we have listed two main image processing techniques used in Currency recognition that is image filtering and image descriptors and detector.
Filtering is a method of altering or enhancing or removing the features of image. The Image processing techniques that are adopted by filtering contains smoothing, sharpening, and edge enhancement. Some of the filtering methods are given below,
- Based on maximum likelihood, determine the value of parameters.
- Remove images without any blurring effects or noises
- In addition to that it has structural risk minimization (SRM)
- Block Matching Collaborative
- It is mainly used to reduce noise from image and then to group similar features of images.
- With the use of collaborative, filtering is carried out on every fragment
Image Descriptors and Detector
It is used to retrieve the fundamental characteristics of images such as shape, texture and color. It returns the descriptions of the attributes of image contents.
- Hessian Affine Region Detector
- It is a feature detector mainly used for image analysis to determine the visible interest points.
- Multiple scale iterative algorithms are used to select and locate the invariant points.
- Deriche edge detector
- It provides optimal edge detection results from 2D images by the use of multistep algorithms.
- In order to remove high frequency noise, the output of the detector is adjusted by scale parameter
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