AI brain recognizing coins

Unlocking Coin Recognition: How AI and Texture Analysis Are Changing Vending Machines and Beyond

Mira Elwood in Tech & Innovation September 2025 • 3 min read.

"Explore how cutting-edge AI techniques are revolutionizing coin recognition, enhancing vending machine accuracy, and transforming automated payment systems."

Coins are integral to our daily transactions, and the need for efficient, automated coin recognition systems has grown exponentially. These systems power vending machines, self-checkout kiosks, and various automated payment platforms, enhancing convenience and speed. However, achieving high accuracy in coin recognition presents significant challenges.

Traditional methods often rely on physical properties such as size and weight. These approaches are prone to errors due to similarities between different coin denominations and the potential for foreign objects or damaged coins. Modern systems are evolving to leverage advanced image processing and pattern recognition techniques to overcome these limitations.

This article delves into innovative approaches using texture-based analysis and artificial intelligence to improve coin recognition accuracy. We will explore the methodologies, experimental results, and future possibilities of this evolving technology, offering insights into how these advancements are transforming automated systems.

The AI-Powered Approach to Coin Recognition

The automated detection of coins is significantly advanced by analyzing image features using Gabor filters, standard invariant moments, and hybrid invariant moments. This method enhances the capacity to discern minute textural details, which are crucial for precise coin identification.

The initial stage, preprocessing, is essential for refining the image data to highlight features relevant to coin recognition. This involves:

Greyscale Conversion: Converting color images to grayscale to simplify analysis while retaining essential features.
Noise Filtering: Applying Gaussian filters to eliminate noise and enhance image clarity.
Contrast Stretching: Improving image contrast to make textural details more distinct.

Following preprocessing, feature extraction is performed using Gabor filters and moment invariants. Gabor filters capture texture information by convolving the image with a set of filters at different orientations and frequencies. Moment invariants, including standard and hybrid types, provide robust descriptors invariant to rotation, scale, and translation, ensuring accurate recognition regardless of coin orientation or size.

Future of Coin Recognition: Color, Texture, and Shape

The integration of color analysis, advanced texture descriptors, and shape recognition promises a future where automated systems can identify coins with near-perfect accuracy. These innovations will not only enhance the performance of vending machines and self-checkout systems but also open new possibilities for financial technologies and automated services.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1109/iccece.2017.8526206, Alternate LINK

Title: Texture Based Coin Recognition Using Multiple Descriptors

Journal: 2017 International Conference on Computer, Electrical & Communication Engineering (ICCECE)

Publisher: IEEE

Authors: Jyotismita Chaki, Ranjan Parekh

Published: 2017-12-01

Everything You Need To Know

What is the importance of preprocessing in AI-powered coin recognition systems, and what steps does it involve?

In the context of automated coin detection, preprocessing is essential for enhancing image data. It refines images by converting them to grayscale, which simplifies analysis while preserving key details. It also employs Gaussian filters to remove noise and contrast stretching to accentuate textural details. These steps collectively prepare the image for more accurate feature extraction.

How do Gabor filters contribute to the automated detection of coins?

Gabor filters are used to extract texture information by processing the image with multiple filters set at various orientations and frequencies. These filters enhance subtle texture differences, which are crucial for accurately identifying coins. Without Gabor filters, systems might struggle to distinguish between similar coin types based on texture alone.

What are moment invariants, and why are they useful in coin recognition?

Moment invariants, including standard and hybrid types, are descriptors that remain unchanged regardless of a coin's rotation, scale, or translation in an image. This is important because coins might not always be perfectly aligned or positioned consistently when scanned. Using moment invariants ensures accurate coin recognition despite variations in orientation or size, adding a level of robustness to the system.

What future advancements in coin recognition are anticipated, and how will they improve automated systems?

The article suggests future coin recognition systems will use color analysis, along with advanced texture descriptors, and shape recognition. Systems leveraging these technologies promise a future where automated systems can identify coins with near-perfect accuracy. These improvements not only enhance vending machines and self-checkout systems but also pave the way for advancements in financial technologies and automated services.

Why are traditional methods that rely on physical properties for coin recognition not sufficient, and what replaces them?

The limitations of relying solely on physical properties like size and weight for coin recognition include susceptibility to errors because of similarities between denominations and potential confusion caused by foreign objects or damaged coins. Modern systems overcome this by using image processing and pattern recognition techniques, such as texture analysis and AI, for more accurate results.