Pokémon TCG Search Engine: Use AI to Catch Them All

With a new custom image similarity service, we are able to build an image search engine for collectible cards trading.
Michal Lukáč, Ximilar
Michal Lukac
11. October 2022
Card search engine by Ximilar

Have you played any trading card games? As an elementary school student, I remember spending hundreds of hours playing Lord of the Rings TCG with my good friend. Back then, LOTR was in the cinemas, and the game was simply fantastic, with beautiful cards. I played with a combination of Ents and Nazguls.

Other people in our office spent their youth playing Magic The Gathering, and collecting sports cards with their favourite players. I personally liked the basketball cards. Cards are still loved, played, collected, and traded by geeks, collectors, and sports fans across the world! Their market is growing, and so is the need for automation of image processing on websites and apps for collectors.

Contents:


Where can you use visual AI for cards?

Trading card games (トレーディングカード) can consist of tens of thousands of cards. In principle, building a basic image classifier based solely on image recognition leads to low precision and is simply not enough for more complicated problems.

However, we are able to build a complex similarity system that can recognize, categorize, and find similar cards by a picture. Once trained properly, it can deal with enormous databases of images it never encountered before. With this system, you can find all the information, such as the year of release, card title, exact value, card set, or whether it already is in someone’s collection, with just a smartphone image of the card.

Tip: Check out our Computer Vision Platform to learn about how basic image recognition systems work.

Collectibles are a big business and some cards are really expensive nowadays. Who knows, maybe you have the card of Charizard or Kobe Bryant hidden in your old box in the attic. We can develop a system for you that can automatically analyze the bulk of trading cards sent from your customers.

Automatic Recognition of Collectibles

Ximilar built an AI system for collectibles’ detection, recognition and more.

What can visual search do for the trading cards market?

In the last year, we have been building a universal system able to train visual models with numerous applications in image search engines. We already offer visual search services for photo search. But, they are optimized mostly for general and fashion images.

In the last decades, we have all witnessed the growth of the TCG community. However, technologies based on artificial intelligence have not yet been used in this market. Plus, even though the first system for scanning trading cards was released by ebay.com, it was not made available for small shops as API. And since trading card games and visual AI are a perfect match, we are going to change itwith a card image search.

Tip: Check out Visual Product Search to learn more about visual search applications.

Which TCG cards could visual AI help with?

An image search engine is a great approach when the number of classes for the image classification is high (above 1,000+). With TCGs, each card is representing a unique class. A convolutional neural network (CNN) trained as a classifier can have poor results when working with a larger number of classes.

Pokémon TCG contains more than 10,000 cards (classes), Magic the Gathering (MTG) over 50.000, and the same goes for basketball or any other sports cards. So basically, we can build a visual search system for both:

  • Trading card games (Magic the Gathering, Lord of the Rings, Pokémon, Yu-Gi-Oh!, Warhammer, and so on)
  • Collectible sports cards (like Ice Hockey, Football, Soccer, Baseball, Basketball, UFC, and more)
Pokémon, Magic The Gathering, LOTR, Ice Hockey and Basketball cards.
Pokémon, Magic The Gathering, LOTR, Ice Hockey, and Basketball cards.
Yes, we are big fans of all these things 🙂

A similar image recognition technology is starting to be used on E-bay when listing trading and sports cards for sale. However, this is only available in the e-bay app on smartphones. The app has a built-in scanning tool for cards and can find the average price with additional info.

Our service for card image search can be integrated into your website or application. And you can simply connect via API through a smartphone, computer, or sorting machine to find exact cards by photo, saving a lot of time and improving the user experience!

We’ve been recently training a CNN model for Pokémon trading cards image recognition. Why Pokémon? Pokémon is the most played TCG in the world, the game has simple rules and an enormous fan base. Some cards are really expensive, but more importantly, they are traded heavily!

With this model, we built a reverse search for finding the exact Pokémon card, which achieved 94%+ accuracy (i.e. exact image match). And we are still talking about a prototype developed in a few days that can improve to almost 100 %.

Automatic grading and inspection of cards with AI

A lot of companies are grading sports & trading cards manually. Our visual AI can be trained to detect corner types, scratches, surface wear, light staining, creases, focus, and borders. The Image recognition models are able to identify marks, wrong cut, lopsided centering, print defects and other special attributes.

For example, PSA is a company that has developed its own grading standards for automatic card grading (MINT). With our platform and team, you can automatize the entire workflow of grading with just one photo.

PSA graded baseball card. Our machine learning model can analyze picture of these cards.
PSA graded baseball card. Automatic grading is possible with machine learning.

With the new custom similarity service, we are able to create a custom solution for trading card image search in a matter of weeks. The process for developing it is quite simple:

  1. We will schedule a call and talk about your goals. We will agree on how we will obtain the training data that are necessary to train your custom machine-learning model for the search engine.
  2. Our machine-learning specialists will assemble a testable image search collection and train a custom machine-learning model for you in a matter of weeks.
  3. After meeting all the requirements of PoC, we will deploy the system to production, and you can connect to it via Rest API.

Image Recognition of Collectibles

Machine learning models bring endless possibilities not only to pop culture geeks and collectors, but to all fields and industries. From personalized recommendations in custom fashion search engines to automatic detection of slight differences in surface materials, the visual AI gets better and smarter every day, making routine tasks a matter of milliseconds. That is one of the reasons why it is an unlimited resource of curiosity, challenges, and joy for us, including being geeks – professionally :).

Ximilar is currently releasing on a ready-to-use computer vision service able to recognize collectibles such as TCG cards, coins, banknotes or post stamps, detect their features and categorize them. Let us know if you’d like to implement it on your website!

Michal Lukáč, Ximilar

Michal Lukáč ML Engineer & Co-founder

Michal is a co-founder of Ximilar and a machine learning expert focusing mainly on image recognition, visual search and computer vision. He is interested in science, loves reading books and choking people at Brazillian Jiu-Jitsu trainings.

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