The work of any coin recognition app, including Coin ID Scanner or CoinSnap, starts with one action: the user taking a picture of the coin, this seemingly easy step beginning a complex series of preparation actions needed for correct identification and check value of coins.
Initial Image Capture
When the user takes a picture using their mobile device camera, the app receives a digital file, the file having an array of pixels and these pixels containing color and brightness information, this original image serving as the main data for the program to use.
Separating the Object from the Background
The first job for the app is separating the coin from all background elements like a table, a hand, or a cloth, this separation process being called segmentation, the program analyzing colors and contrasts for finding the coin’s clear outer edge.
Using algorithms for finding a closed round or almost round border, the program determines the likely coin position, then creating a digital mask that covers everything outside this border, keeping only the image of the coin for the next steps.
Correcting Perspective and Alignment
Taking a photograph of a coin, a person rarely holds the phone perfectly parallel to its flat surface, this common practice causing distortions where a round coin looks like an ellipse or a trapezoid, meaning the app must correct this error.
Calculating Distortion: Based on the detected edges of the coin, the program calculates the level of tilt and deformation, this calculation showing how the image is mathematically wrong.
Digital Straightening: Applying a reverse mathematical transformation, the app digitally “stretches” or “squeezes” the coin image in necessary places, this action making the coin perfectly round again, as if the picture was taken from an ideal angle, the final output providing a standard, geometrically correct view of the coin needed for accurate comparison in the following stages.
Normalizing Light and Color
People photograph coins in many different light conditions, for example, under dim light, bright sun, or a fluorescent lamp, this varied lighting changing the visible color and contrast, forcing the app to normalize the colors.
Adjusting Brightness: Analyzing the brightness distribution across the coin, the program adjusts it for removing strong shadows and overly bright areas, this process making the image look more even.
Color Correction: The app aims to bring the coin's colors to a "standard" look, this standardization being important because the color of the patina, meaning the metal's oxidation, is a key factor for judging condition, this normalization ensuring that a copper coin photographed under yellow light is not mistakenly identified as gold or brass.
Digital Recognition and Comparison
Being prepared, cleaned, and straightened, the coin image is now ready for the central recognition system, this system being the most complex part of the app, relying on deep learning technology and using special neural networks.
Digital Fingerprint of the Coin
Starting layer by layer, the neural network looks at the coin image, searching for simple elements like lines, corners, or round shapes in the first layers.
In the deeper layers, the network combines these simple elements into complex features, recognizing the shape of a portrait, the style of the letters, the look of the coat of arms, or the design of the border.
Finishing this process, the neural network changes all these visual features into a long number code, this code being called the feature vector, this code being a unique digital "fingerprint" of the coin, meaning two identical coins will have almost the same code, while two different coins will have codes that are very different.
Finding Matches in the Database
This unique number code, the feature vector, is sent for comparison with a huge digital library of standard coins stored on the app’s servers, this server holding all the necessary reference data.
The program compares the received code with the codes of all the standard coins in its database, performing the comparison using mathematical rules for measuring the distance between the number codes, this distance measurement showing the degree of matching.
The result of the comparison is a list of the most similar coins from the database, the program showing a percentage match for each one, for example, a 99.7% match with Coin A, and an 85% match with Coin B, this list giving the user clear options.
The coin with the highest percentage match, for instance, 99.7%, is declared identified, the program retrieving its full set of facts: the country, the value, the material, and the design.
Grading
After the basic identification of the coin type is done, the app moves to finer points, such as the year of issue and, most importantly, the level of its preservation, this preservation level greatly affecting its collector value.
Optical Character Recognition
Coins of the same type may be issued for many years, their value often depending on the year or the presence of a mint mark, requiring the app to read the symbols.
Using special algorithms, the app determines the exact place of the year of issue and the mint mark on the coin's surface, ensuring the correct area is checked.
A separate module, similar to those used for reading numbers on car license plates, starts working, recognizing the numbers and letters, even if they are rubbed or slightly damaged.
The recognized value, for example, "1945," is checked against the list of allowed dates for this specific coin in the database; this check prevents errors caused by unclear text.
Assessing Preservation
The program focuses on parts that wear out first from being used, such as the highest points of the design, small details on a portrait, the tips of wings, or thin lines in the writing.
The neural network compares the condition of these key areas with standard images of the coin in perfect condition, with wear appearing as smoothing or the complete loss of these thin details, meaning the less detail is saved, the lower the grade will be.
The app analyzes the coin for mechanical damages like scratches, dents, or cleaning marks, and natural changes like the evenness and color of the patina.
Based on the combined analysis of wear and damage, a number or letter grade is assigned to the coin, this grade following international coin standards, such as F-12, AU-50, or MS-65, which tell collectors about the coin's quality.
Result and Price Information
At the final stage, all the gathered information is put together for the user to see, this presentation giving the full picture.
Using the coin's unique identifier and the grade assigned to it, the app contacts its structured numismatic database, this database having all the important facts.
Full Description: All standard data is retrieved, including the coin's full name, the history of its issue, the total number produced, and its physical facts like weight and size.
Price: The most important thing is the market value information, with the database having dynamic price data that is strictly linked to the degree of preservation, the value of a coin with an "MS-65" grade, meaning almost perfect, being very different from the value of the same coin with a "Good" grade, meaning heavily worn.
All this data is presented to the user as a clear and organized report on their device screen, the report including:
The coin image in its standardized form
The exact identification, listing the country, value, year, and mint mark
The determined grade of preservation
The corresponding market value