Automated Coin Recognition System with Rotation Invariance

Problem Definition

PROBLEM DESCRIPTION: The problem that can be addressed using this project is the need for an efficient and accurate Automated Coin Recognition System. Currently, coin recognition systems and coin sorting machines are widely used in various industries such as banks, supermarkets, grocery stores, and vending machines. However, there is a need for a system that can accurately recognize coins of various denominations (`1, `2, `5, and `10) with rotation invariance. Traditional coin recognition systems may not be able to accurately identify and classify coins due to variations in lighting conditions, rotation angles, and image quality. This project aims to address these challenges by utilizing digital image processing techniques to extract various features of coins such as thickness, weight, and magnetism.

By training the system with a dataset of images and using advanced classification approaches, the system can effectively match new images of coins with the trained dataset to accurately recognize and classify coins. In addition, the proposed system can also serve as an authentication system to verify the reliability and authenticity of coins, which is crucial in ensuring the security and integrity of monetary transactions. Overall, the goal of this project is to develop a robust Automated Coin Recognition System that can accurately classify coins and provide reliable results, ultimately improving the efficiency and accuracy of coin recognition processes in various industries.

Proposed Work

The proposed work focuses on the development of an Automated Coin Recognition System using advanced classification approaches in digital image processing. The system aims to accurately recognize coins of denominations `1, `2, `5, and `10 with rotation invariance. The project utilizes various image processing techniques to extract features such as thickness, weight, and magnetism from coin images. The system is trained using a dataset of coin images and then tested with a new dataset for matching purposes. The recognition is based on the minimum difference between the images.

This system not only serves the purpose of coin recognition but also acts as an authentication system to ensure the reliability of coins in circulation. The project utilizes modules such as Regulated Power Supply, IR Reflector Sensor, and basic MATLAB, including a MATLAB GUI for user interaction. This research falls under the categories of Image Processing & Computer Vision and MATLAB Based Projects, specifically focusing on Feature Extraction, Image Classification, and Image Recognition. The work will be validated through simulations on MATLAB, demonstrating the efficiency and accuracy of the proposed coin recognition system.

Application Area for Industry

The Automated Coin Recognition System proposed in this project can find applications in various industrial sectors such as banking, retail, and vending. In the banking sector, this system can be used to accurately and efficiently sort and recognize coins during cash handling processes, reducing manual errors and speeding up transactions. In retail industries, such as supermarkets and grocery stores, the system can be integrated into self-checkout machines to automatically recognize coins during payment, providing a seamless and convenient shopping experience for customers. Vending machines can also benefit from this system by accurately recognizing and validating coins inserted by customers to dispense products. The proposed solutions in this project address specific challenges faced by industries in accurately recognizing and classifying coins under varying conditions such as lighting, rotation angles, and image quality.

By utilizing advanced digital image processing techniques and training the system with a dataset of coin images, the system can effectively match new coin images to accurately recognize and classify coins. In addition, the system can serve as an authentication tool to verify the authenticity of coins, ensuring the security and reliability of monetary transactions. Implementing this Automated Coin Recognition System in various industrial domains can result in improved efficiency, accuracy, and security in coin recognition processes, ultimately enhancing the overall operational performance of industries.

Application Area for Academics

The proposed project on Automated Coin Recognition System offers a valuable opportunity for MTech and PHD students to engage in innovative research methods, simulations, and data analysis within the fields of Image Processing & Computer Vision and MATLAB Based Projects. This project addresses the pressing need for an efficient and accurate coin recognition system that can recognize coins of various denominations with rotation invariance. By utilizing digital image processing techniques to extract features such as thickness, weight, and magnetism from coin images, the system can effectively match new images of coins with a trained dataset to accurately recognize and classify them. This project not only enhances the efficiency and accuracy of coin recognition processes in industries like banking and retail but also provides a platform for scholars to explore advanced classification approaches in image processing. MTech students and PHD scholars can use the code, methodology, and literature of this project for their research, dissertations, theses, or research papers in the areas of Feature Extraction, Image Classification, and Image Recognition.

The future scope of this project includes potential applications in authentication systems for verifying the reliability and authenticity of coins, further expanding its relevance and impact in the research community.

Keywords

Keywords: Automated Coin Recognition System, Coin Recognition, Coin Sorting Machine, Digital Image Processing, Rotation Invariance, Coin Denominations, Image Quality, Feature Extraction, Thickness, Weight, Magnetism, Dataset, Classification Approaches, Authentication System, Monetary Transactions, Efficiency, Accuracy, Image Processing Techniques, Regulated Power Supply, IR Reflector Sensor, MATLAB GUI, Image Classification, Image Recognition, Computer Vision, Feature Extraction, Neural Network, SVM, Latest Projects, New Projects, Image Acquisition.