Indian Currency Recognition using Machine Learning
Currency Detection and Recognition using Image Processing
A Currency Detection and Recognition System (CDRS) is a technology that enables machines or computer systems to automatically identify and distinguish different types of currencies. This system is particularly useful in various applications, such as automated teller machines (ATMs), currency exchange counters, vending machines, and mobile payment platforms
The CURRENCY DETECTION AND RECOGNITION SYSTEM (CDRS) USING PYTHON involves building a computer vision-based system to automatically identify and classify different currencies. Here’s a brief overview of the process:
1.Image Acquisition: The system captures an image of the currency, either from a camera or by loading an image file.
2.Pre-processing: The acquired image undergoes pre-processing to enhance its quality and remove any noise or distortion. Common pre-processing steps include converting the image to grayscale, applying filters, and resizing.
3.Text and Object Detection: The system identifies and locates text (currency denomination and symbols) and other relevant objects, such as security features. Optical Character Recognition (OCR) techniques can be used for text detection.
4.Feature Extraction: Relevant features, such as patterns, colors, and other distinguishing characteristics, are extracted from the pre-processed image. These features will be used for classification.
5.Classification: Machine learning algorithms, such as Support Vector Machines (SVM), Random Forest, or Convolutional Neural Networks (CNN), are used to classify the currency into its corresponding denomination and type.
6.Recognition and Authentication: After classification, the system recognizes the currency denomination and type. Additionally, it may include authentication steps to verify the legitimacy of the currency, using counterfeit detection techniques if required.
7.User Interface: The system may have a user interface to display the recognized currency denomination and other relevant information to the user.
CONCLUSION
The CURRENCY DETECTION AND RECOGNITION SYSTEM (CDRS) USING PYTHON enables efficient and accurate automated identification of different currencies, streamlining currency handling processes in various applications like ATMs and currency exchange counters.
THis video is about Currency Recognition using Image Processing, this project trained on large dataset of indian currency images that will be downloaded from roboflow website. dataset contains images of different different notes and their images and this datsaset is labelled dataset because this dataset has boundary boxes it will make detection and recognition and in this project we have used yolo algorithm. User have to upload note image to machine learning model and it will detect note and recognise it.
Fake Indian Currency Detection using Machine Learning
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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