The newest addition to our portfolio of solutions is the Comics Identification (/v2/comics_id). This service is designed to identify comics from images. While it’s still in the early stages, we are actively refining and enhancing its capabilities.

The API detects the largest comic book in an image, and provides key information such as the title, issue number, release date, publisher, origin date, and creator’s name, making it ideal for identifying comic books, magazines, as well as manga.

This tool is perfect for organizing and cataloging large comic collections, offering accurate identification and automation of metadata extraction. Whether you’re managing a digital archive or cataloging physical collections, the Comics Identification API streamlines the process by quickly delivering essential details. We’re committed to continuously improving this service to meet the evolving needs of comic identification.

Star Wars Unlimited, Digimon, Dragon Ball, and More Can Now Be Recognized by Our System

Our trading card identification system has already been widely used to accurately recognize and provide detailed information on cards from games like Pokémon, Yu-Gi-Oh!, Magic: The Gathering, One Piece, Flesh and Blood, MetaZoo, and Lorcana.

Recently, we’ve expanded the system to include cards from Garbage Pail Kids, Star Wars Unlimited, Digimon, Dragon Ball Super, Weiss Schwarz, and Union Arena. And we’re continually adding new games based on demand. For the full and up-to-date list of recognized games, check out our API documentation.

Detect and Identify Both Trading Cards and Their Slab Labels

The new endpoint slab_grade processes your list of image records to detect and identify cards and slab labels. It utilizes advanced image recognition to return detailed results, including the location of detected items and analyzed features.

The Slab Label object provides essential information, such as the company or category (e.g., BECKETT, CGC, PSA, SGC, MANA, ACE, TAG, Other), the card’s grade, and the side of the slab. This endpoint enhances our capability to categorize and assess trading cards with greater precision. In our App, you will find it under Collectibles Recognition: Slab Reading & Identification.

Automatic Recognition of Collectibles

Ximilar built an AI system for the detection, recognition and grading of collectibles. Check it out!

Read more

New Endpoint for Card Centering Analysis With Interactive Demo

Given a single image record, the centering endpoint returns the position of a card and performs centering analysis. You can also get a visualization of grading through the _clean_url_card and _exact_url_card fields.

The _tags field indicates if the card is autographed, its side, and type. Centering information is included in the card field of the record.

Learn How to Scan and Identify Trading Card Games in Bulk With Ximilar

Our new guide How To Scan And Identify Your Trading Cards With Ximilar AI explains how to use AI to streamline card processing with card scanners. It covers everything from setting up your scanner and running a Python script to analyzing results and integrating them into your website.

Let Us Know What You Think!

And that’s a wrap on our latest updates to the platform! We hope these new features might help your shop, website, or app grow traffic and gain an edge over the competition.

If you have any questions, feedback, or ideas on how you’d like to see the services evolve, we’d love to hear from you. We’re always open to suggestions because your input shapes the future of our platform. Your voice matters!

The post New AI Solutions for Card & Comic Book Collectors appeared first on Ximilar: Visual AI for Business.

Our AI-powered Product Description revolutionizes the way you manage your fashion apparel catalogs by fully automating the creation of product titles and descriptions. Instead of spending hours manually tagging and writing descriptions, our AI-driven generator swiftly produces optimized texts, saving you valuable time and effort.

With the ability to customize style, tonality, format, length, and preferred product tags, you can ensure that each description aligns perfectly with your brand’s voice and SEO needs. This service is designed to streamline your workflow, providing accurate, engaging, and search-friendly descriptions for your entire fashion inventory.

Enhanced Taxonomy for Accessories Product Tagging

We’ve upgraded our taxonomy for accessories tagging. For sunglasses and glasses, you can now get tags for frame types (Frameless, Fully Framed, Half-Framed), materials (Combined, Metal, Plastic & Acetate), and shapes (Aviator, Cat-eye, Geometric, Oval, Rectangle, Vizor/Sport, Wayfarer, Round, Square). Try how it works on your images in our public demo.

Automate Detection & Tagging of Home Decor Images With AI

Our new Home Decor Tagging service streamlines the process of categorizing and managing your home decor product images. It uses advanced recognition technology to automatically assign categories, sub-categories, and tags to each image, making your product catalog more organized. You can customize the tags and choose translations to fit your needs.

The service also offers flexibility with custom profiles, allowing you to rename tags or add new ones based on your requirements. For pricing details and to see the service in action, check our API documentation or contact our support team for help with custom tagging and translations.

Visual Search for Home Decor: Find Products With Real-Life Photos

With our new Home Decor Search service, customers can use real-life photos to find visually similar items from your furniture and home decor catalogue.

Our tool integrates four key functionalities: home decor detection, product tagging, colour extraction, and visual search. It allows users to upload a photo, which the system analyzes to detect home decor items and match them with similar products from your inventory.

To use Home Decor Search, you first sync your database with Ximilar’s cloud collection. This involves processing product images to detect and tag items, and discarding the images immediately after. Once your data is synced, you can perform visual searches by submitting photos and retrieving similar products based on visual and tag similarity.

The API allows for customized searches, such as specifying exact objects of interest or integrating custom profiles to modify tag outputs. For a streamlined experience, Ximilar offers options for automatic synchronization and data mapping, ensuring your product catalog remains up-to-date and accurate.

The post New Solutions & Innovations in Fashion and Home Decor AI appeared first on Ximilar: Visual AI for Business.

Today, I’d like to introduce a solution specifically designed for physical stores and warehouses to process their physical card collections quickly and efficiently using card scanners like those from Fujitsu. This tutorial is tailored for shop owners who need to handle large volumes of card images rapidly. We’ve developed a simple yet powerful script in Python 3 for card identification, condition assessment or grading. It also identifies comic books and reads slab labels from companies like PSA or Beckett. The script outputs a CSV file that can be easily imported into Google Sheets or Microsoft Excel. With a few modifications, it can also be adapted for use with your Shopify store or other seller tools, such as for eBay submissions. Let’s dive in and see how this tool can streamline your card-processing workflow!

Capabilities of our AI Solution for Sports Cards and TCGs

Trading Card Games

In the previous blog post, I wrote about our REST API for identifying TCGs, sports cards, and comic book covers. The TCG identification service supports more trading card games, including the most popular ones like Pokémon, Yu-Gi-Oh!, Magic: The Gathering, One Piece, and Lorcana. For some games, it can also identify the correct language version of the card or determine if it is a foil/holographic card. Additionally, for certain TCG games, the system provides links or identification numbers to the TCG Player. You can try how it works here.

Sports Cards

For sports cards, we can identify more than 5 million trading cards across six main sports categories: baseball, hockey, football, soccer, MMA, and basketball cards. Our system also supports the identification of parallel and reprint versions, with continuous improvements. Not only does it provide the best match, but it also offers alternative options to choose from.

If the trading cards are in slabs from major grading companies like PSA, Beckett, CGC, TAG, SGC, or ACE, the system can instantly identify graded cards and provide the slab company, grade, and certificate number.

All Under One API

As you can see, the functionality is complex, offering features such as bulk trading card scanning and language support, resulting in highly accurate identification. I believe that Ximilar Collectibles Recognition services are the most accurate solutions available on the market today. It is a true game-changer for card dealers, other collectors, or companies looking to be independent of third parties like CollX, Kronozio, or Card Dealer Pro, which automatically submit your cards to their marketplaces.

With Ximilar, you can handle your trading card scanning independently using our visual search technology and deep learning models. Our solutions are also designed to suit your specific needs through continuous improvements and customization. Whether you purchase, scan, analyze, search, or sell cards in bulk, our API empowers you to manage your collection without the constraints of third-party services.

How to Analyze TCG and Sports Card Scanners With AI

Step 1 – Run The Cards Through The Scanner

Enough talk! Let’s analyze the bulk of your cards. First, you’ll need a folder with images of your cards. For testing, I’ve selected a small MTG and Pokémon card subset. You can put them on your scanner via top loader (link), or individually. Most card collectors use the Fujitsu Ricoh Fi-8170 scanner, which is one of the best scanners available. It can capture both the front and back sides of the cards.

For our purposes, we will only need the front side of the cards. To avoid unnecessary costs, remove the back side images from the folder or configure your scanner to store only the front side of the cards. Some scanners, like Fujitsu, can produce scan files with names such as 19032024-0001.jpg or 19032024-FRONT-0001.jpg. You can specify the naming format for the scan files. See the following video tutorial on how to set up a Fujitsu scanner via PaperStream Capture by MaxWaxPax:

My recommendation is to use similar settings for your Fujitsu scanner as it is in the video by MaxWaxPax and create multiple profiles for sideways and top-bottom trading card scanning. Ideally set up the scanner to produce only images for the front of the cards or distinguish the images with “front” or “back” suffix in the filename. However, if you already have an unstructured collection of card images, you can fully automate the selection of images showing the front sides using our AI Recognition of Collectibles.

Step 2 – Sign Up To Ximilar Platform

Now, you’ll need an account in our App. Simply sign up with your personal or company email to get your unique API token for service authorization. Once you are in the App, copy your API key to the clipboard and save it into some file. To access the service via API, you’ll need to purchase at least a Business plan. Both tasks – getting the API key and purchasing a Business plan – can be completed in the platform’s settings in a matter of minutes.

Step 3 – Installing Python 3

Before running the script, ensure you have Python 3 installed. Some operating systems already include a version of Python, but we require at least Python 3.6. If you’re unsure, follow this tutorial on RealPython (link), which contains installation steps for Windows, macOS, and Linux:

You should be able to write in your command line, shell or terminal the similar command. Here’s mine at Mac:

michallukac@Michals-MacBook ~ % python --version && pip --version

If you don’t know how to run commands, read a short tutorial on using the terminal/shell/command line. I recommend this tutorial by DjangoGirls or watching some YouTube videos (here’s one for Windows and one for macOS). The output from the command should look similar to my example:

Python 3.9.18

pip 23.1 from /Users/michallukac/env/devel/lib/python3.9/site-packages/pip (python 3.9)

Next, you will need to install Python libraries argparse and requests via pip command:

pip install --upgrade argparse

pip install --upgrade requests

If everything passes, you’re now ready to use the script we’ve prepared to process your folder of card images!

Step 4 – Running The Script On Trading Card Games

Running the script is simple. You’ll need to use a terminal (macOS), shell (Linux), or command line (Windows), which is why we installed Python 3. Download the following file from one of these addresses:

• https://gitlab.com/ximilar-public/ximilar-client/-/blob/master/tools/process_card_scans.py?ref_type=heads

• https://gitlab.com/ximilar-public/ximilar-client/-/raw/master/tools/process_card_scans.py?ref_type=heads&inline=false

Put this file/script next to the folder (tcgscans) with your trading card images or scans and in the terminal, write the following command:

python process_card_scans.py --folder tcgscans --api_key YOURAPIKEY --collectible tcg --output results.csv --select_images all

Hitting the enter will execute the script on the folder of tcgscans, and the progress bar will be shown. The folder will analyze all the images in the folder (select_images). You can interrupt the script (it automatically stores the results every 10 images to your specified output CSV file):

Each analysis of a scan (sports card) will consume 10 credits from your credit supply in your Ximilar account. Our App lets you watch your credit consumption closely under Reports. The Business 100k Plan allows you to analyze 10,000 raw cards. If you need to analyze millions of cards per month or your entire collection at once, reach out to us, and we can offer you a bulk discount.

Step 5 – Analyzing the CSV file

Now we have our CSV file named results.csv. The CSV file contains the following fields: filename (name of the photo in the folder), status (ok or error), side (front or back), subcategory, full_name, name, year, card_number, series, set, set_code, and other additional fields.

The output format of the CSV depends on whether you analyze sports cards, TCG cards, comics, or slabs. Here is a visualization of the CSV file in Visual Studio Code:

We can import the file into Google Sheets or Microsoft Excel spreadsheet, edit it as needed, or generate printable checklists. The columns and data from the CSV can also be easily added to your Shopify product files or used for eBay submissions.

Additional information for card condition (or grading) can be added to the script via the –condition (–grading) parameter. For example, if your sports card scanner provides images with filenames such as 0001.jpg, 0002.jpg, 0003.jpg, etc., the following command will process images with odd numbering (e.g., 0001.jpg, 0003.jpg, …), identify the cards (name, card number, etc.), and also compute their condition (very good, excellent, etc.):

python process_card_scans.py --folder sportsfolder --api_key YOUR_API_KEY --collectible sport --output sport.csv --select_images odd --alternative --condition

Conclusion

With Ximilar’s AI-powered solutions, identifying and documenting your trading cards has never been easier. From trading card scanning, analyzing and organizing, to finding the current average market price, every step is streamlined to save you time and effort. I hope this guide helps you optimize your trading card workflow, making it easier to manage and showcase your collection. Happy collecting, whether it’s baseball or Pokémon cards!

The post How To Scan And Identify Your Trading Cards With Ximilar AI appeared first on Ximilar: Visual AI for Business.

Today we are releasing three public API endpoints for evaluating card grade, centering and card condition with AI:

• Card Grading – the most complex endpoint that evaluates corners, edges, surface and centering
• Card Centering – computing just the centering of the card
• Card Condition – simple API for getting condition of the card for marketplace (ebay) submission

In this blog post, I would like to get more in-depth about the AI card grading solution. How we built it, what are the pros and cons, how it is different from PSA grading or Beckett grading services, and how you can use it via REST API for your website or app.

AI Card Grading Services as API

With the latest advances in artificial intelligence, it is becoming increasingly common in our daily lives, and collectible cards are a field that doesn’t get left behind. A lot of startups are developing their own card grading, identification, scanning and documenting systems. Some of them were already successfully sold to big players like eBay or PSA. Just to mention a few:

• CollX raises $5.5M to scan and evaluate the value of trading cards

• eBay acquires several startups from the collectors industry like TCGplayer and 3PM Shield LLC

• Collectible card trading platform Alt raises $75M

• PSA Acquires Genamint to introduce Next-Generation Grading process

• AI trading card startup Ludex raises $8M

To understand why card grading is so popular, let’s look at the standard grading process and how the industry works.

Standard Grading Process

Card grading has gained widespread popularity in the world of collectibles by offering a trusted way to assess trading cards to collectors. It’s a method that gives a fair and unbiased evaluation of a card’s condition, ensuring its authenticity and value. This appeals to both seasoned collectors who want to preserve their cards’ worth and newcomers looking to navigate the collectible market confidently.

The process involves sending cards to experts who carefully inspect them for qualities like centering, corners, edges, and surface. The standard grading process for trading cards involves these key steps:

1. Submission: Collectors send their cards to grading companies.

2. Authentication: Cards are checked for authenticity.

3. Grading: Cards are assessed for condition and assigned a grade from 1 to 10 on a grading scale by an expert.

4. Encapsulation: Graded cards are sealed in protective holders.

5. Labelling & Certification: Labels with card details and grades are added. Cards’ information is recorded for verification. Special labels (such as fugitive ink, QR codes, or serial numbers) are introduced to prevent tampering.

6. Return/Sale: Graded cards are returned to owners or sold for higher value.

Costs of Grading Services

The price for submitting cards and their grading depends on the company and the card. For example, the minimal grading price per card by PSA (Professional Sports Authenticator) is 15 USD, and it’s much more for more expensive cards.

You can pay hundreds of dollars if you have some rare baseball card from Topps or non-sports cards from Magic The Gathering or Yu-Gi-Oh! If your modern card collection contains hundreds of cards, the pricing can reach astronomical values. Of course, grading often makes the card’s value higher, depending on its condition and grade.

Pros And Cons of Classic Grading

Besides its costliness, classic grading has several other drawbacks:

• It is a time-consuming offline process that is not particularly ideal for large-scale grading of whole collections.
• Some grading companies would only grade cards with minimum submission value (declared value that is used for insurance).
• Also, customers can usually submit only cards from popular series such as Pokémon, Magic The Gathering, Yu-Gi-Oh!, Sport Topps cards, and Sport Panini cards.

Of course, there are also advantages – like a physically sealed slab with a graded card, confirming its authenticity, and grading done by experts who can look at a card from all different angles and not just from a single image.

Nevertheless, there are a lot of steps involved in card grading, and the entire process takes a lot of time and effort. AI grading can help with the entire workflow, from authentication to grading and labelling.

Computer vision can easily and consistently spot printing defects, analyze corners and edges individually and compute centering in a matter of seconds and for a fraction of the price.

Introducing Online AI Card Grading REST API Service

Fast & Affordable AI Card Grading

Our intention is by no means to replace expert grading companies like PSA, BGS, SGC or CGC with AI-powered card grading. We would rather like it to be a faster, more consistent & cheaper alternative for anyone who needs bulk pre-grading of their collections.

One use case for our AI grading service is to use it to automate the estimation of the declared value of the card. A declared value is the estimated value of the collectible card after PSA has graded it (read PSA’s explanation here).

First, you will submit your card for grading by just sending the photo to our API. After obtaining a grade from our service, you can use our visual search system or card ID for a price guide. Actually, you will not only get the final grade of the card but a detailed grading breakdown (for edges, corners, centering, and surface). Then you can decide by yourself if you want to spend more money for physical grading or to sell it on eBay.

How Do We Train AI to Grade Cards?

To build an AI grading system powered by computer vision and machine learning techniques, we needed a lot of data that imitated real-world use cases (usually user-generated content such as smartphone pictures).

We spent a lot of time building our own dataset, including damaging our own cards. Our purpose from the beginning was to have a grader that would work both on sports cards and trading card games (TCGs), as well as images of different qualities and with different positioning of the cards.

AI Card Grader Consists of Several AI Models

Our card grading solution integrates a number of machine learning models trained on specific datasets. After you upload a photo of a card, the system needs to be able to correctly detect its position. It then identifies the type of the card: a sports card or a trading card game. Another recognition model identifies whether the picture shows the front or back of the card.

After localization & simple identification, the card gets an individual evaluation of its parts. We trained numerous models for individual grading of corners, edges, card surface, and centering, in accordance with grading standards such as PSA or Beckett.

Of course, different types of cards require a different approach, which is why, for example, we have two different models for corners. While sports cards should have sharp corners, TCG cards are typically more rounded.

From the individual grades, we compute a final grade with condition evaluation. Another model is identifying autographed cards. The cards with autographs are generally more valuable.

The big advantage is that the output of the card grading is easy to visualize. That is why we also provide a simple image with the report for each graded card. There you can see a detailed grading breakdown for every part of the card.

Limitations of AI and Machine Learning in Card Grading

Of course, both humans and AI can make mistakes. There are some limitations of the system. Estimating card grades from the images requires relatively high-resolution images, with good lighting conditions and with low post-processing.

As a matter of fact, a lot of modern cameras in smartphones are currently not very good at close-up photos. Their sensors have gotten bigger over the years, and their AI is upscaling the photos. This makes them artificially sharp with cartoon-like effects. This can of course corrupt the overall results. However, as I previously mentioned, that is why we train the models on real-life images and gradually improve their performance.

Let’s Get Some Cards Graded Via Our Online API

Modern Basketball Card

We can test our AI grader via Ximilar App. For this purpose, I chose one of the classic basketball cards of Michael Jordan. BGS (Beckett) gave this card a grade of 6 (EX-MT).

Our online grading system assigned this card a final grade of 6.5. The centering is quite off, so the system graded it 6/10. The grading is still not perfect, as it misses the surface by quite a large margin. However, the final grade is quite close to the one received by Beckett.

In the breakdown image, you can see how the system evaluated individual parts of the card. The lines are drawn on the image, so you can see the details of individual grades for corners and edges. We hope that this brings more transparency to the algorithmic grading.

Vintage Baseball Card

Now let’s take a look at an image of a vintage sports card without an autograph. As an example, I chose the baseball card with Ed Mathews.

The final grade that the card receives is 6.0. The average corner value assigned by the system is 4.0 and edges are 7.0. The grade for the surface is 5.5 and the centering is 7.0 (left/right is 36/65 and top/bottom is 38/62).

We can take a look at the corners and think whether a professional grader would assign the same values. I personally think that the grade is reasonable. However, getting grades from a single image is hard. We’re also not trying to make the values precise up to decimals (e.g., 4.12453 for the upper left corner). We want this to be an affordable soft pre-grading solution.

Card corners are a bit blurry, so ideally, we would like to have a sharper image. However, we can see that the corners are not in the range of 7–10 grades but rather lower (4-6).

How Do We Compute the Final Grade?

We compute the final grade for corners and edges simply as an average of the individual values. We trained the centering grader according to the Beckett grading scale. It is in our opinion much better (has higher demands) than PSA in this case. So to get 10 points for centering, you need to have a 50/50 ratio – on top/bottom and left/right.

The good thing is, that since we provide values for all parts of the card, you don’t need to use our final grades. You can actually create and use your own formula for computing the final grade.

Card Centering API with AI

Some of our customers would like to compute just the centering of the card. That is why we publish also endpoint for this. It will return you offsets from left, right and top and bottom borders of the card. The offsets are relative and also absolute so you can visualize it in your application. Each API response contains image with visualized centering as part of the output:

Lightweight Grading, alias Card Condition Assessment

For customers that want to submit cards to online marketplaces and need to know just the condition of the card like Near Mint, Lightly Played, Heavily Played or Damaged we offer an additional endpoint for getting rough condition of your card. Because this endpoint (/v2/condition) is much simpler and also significantly cheaper than our /v2/grade endpoint. It’s great for a massive amount of data and suitable for collector shops all over the world. The API endpoint can be called from your application or we can write your own script that is able to analyze images/cards from Fujitsu scanners (Fujitsu FI-8170). If you also want to have a card identification service, our visual search AI can identify the TCGs like Pokemon, Magic The Gathering or Yugioh! with more than 98% accuracy.

You can ask to return the condition in several different formats like TCGPlayer, Ebay or our own.

The more about /v2/condition endpoint can be found in our documentation.

How You Can Test Ximilar Card Grader?

To test our online card grader API, you will need to log into the Ximilar App, where it is currently available to users of all plans for testing purposes. We are also currently working on a public demo.

The system is not perfect, neither is the real human grader. It will take us some time to develop something that will be near perfect and very stable. But I believe that we are on the right track to make AI-powered solutions in the collectibles industry more accessible and cheaper.

To Sum Up

The AI card grader is just one of many solutions by Ximilar that the collector community can use. Make sure to check out our AI Recognition of Collectibles. It is a universal service for the automated detection and recognition of all kinds of collectible items.

Automatic Recognition of Collectibles

Ximilar built an AI system for the detection, recognition and grading of collectibles. Check it out!

Read more

If you would like us to customize any solution for collectors, just contact us and we will get back to you. We created these solutions (Card Identification and Card Grading) to be the best publicly available AI tools for collectors.

The post AI Card Grading – Automate Sports Cards Pre-Grading appeared first on Ximilar: Visual AI for Business.

That is why we in Ximilar have recently developed an OCR system, integrated it with ChatGPT and made it available via API. It is one of the first publicly available services combining OCR software and the GPT model, supporting several alphabets and languages. In this article, I will provide an overview of what OCR and ChatGPT are, how they work, and – more importantly – how anyone can benefit from their combination.

What is Optical Character Recognition (OCR)?

OCR (Optical Character Recognition) is a technology that can quickly scan documents or images and extract text data from them. OCR engines are powered by artificial intelligence & machine learning. They use object detection, pattern recognition and feature extraction.

An OCR software can actually read not only printed but also handwritten text in an image or a document and provide you with extracted text information in a file format of your choosing.

How Optical Character Recognition Works?

When an OCR engine is provided with an image, it first detects the position of the text. Then, it uses AI model for reading individual characters to find out what the text in the scanned document says (text recognition).

This way, OCR tools can provide accurate information from virtually any kind of image file or document type. To name a few examples: PDF files containing camera images, scanned documents (e.g., legal documents), old printed documents such as historical newspapers, or even license plates.

Most OCR tools are optimized for specific languages and alphabets. We can tune these tools in many ways. For example, to automate the reading of invoices, receipts, or contracts. They can also specialize in handwritten or printed paper documents.

The basic outputs from OCR tools are usually the extracted texts and their locations in the image. The data extracted with these tools can then serve various purposes, depending on your needs. From uploading the extracted text to simple Word documents to turning the recognized text to speech format for visually impaired users.

OCR programs can also do a layout analysis for transforming text into a table. Or they can integrate natural language processing (NLP) for further text analysis and extraction of named entities (NER). For example, identifying numbers, famous people or locations in the text, like ‘Albert Einstein’ or ‘Eiffel Tower’.

Technologies Related to OCR

You can also meet the term optical word recognition (OWR). This technology is not as widely used as the optical character recognition software. It involves the recognition and extraction of individual words or groups of words from an image.

There is also optical mark recognition (OMR). This technology can detect and interpret marks made on paper or other media. It can work together with OCR technology, for instance, to process and grade tests or surveys.

And last but not least, there is intelligent character recognition (ICR). It is a specific OCR optimised for the extraction of handwritten text from an image. All these advanced methods share some underlying principles.

What are GPT and ChatGPT?

Generative pre-trained transformer (GPT), is an AI text model that is able to generate textual outputs based on input (prompt). GPT models are large language models (LLMs) powered by deep learning and relying on neural networks. They are incredibly powerful tools and can do content creation (e.g., writing paragraphs of blog posts), proofreading and error fixing, explaining concepts & ideas, and much more.

The Impact of ChatGPT

ChatGPT introduced by OpenAI and Microsoft is an extension of the GPT model, which is further optimized for conversations. It has had a great impact on how we search, work with and process data.

GPT models are trained on huge amounts of textual data. So they have better knowledge than an average human being about many topics. In my case, ChatGPT has definitely better English writing & grammar skills than me. Here’s an example of ChatGPT explaining quantum computing:

It is no overstatement to say that the introduction of ChatGPT revolutionized data processing, analysis, search, and retrieval.

How Can OCR & GPT Be Combined For Smart Text Extraction

The combination of OCR with GPT models enables us to use this technology to its full potential. GPT can understand, analyze and edit textual inputs. That is why it is ideal for post-processing of the raw text data extracted from images with OCR technology. You can give the text to the GPT and ask simple questions such as “What are the items on the invoice and what is the invoice price?” and get an answer with the exact structure you need.

This was a very hard problem just a year ago, and a lot of companies were trying to build intelligent document-reading systems, investing millions of dollars in them. The large language models are really game changers and major time savers. It is great that they can be combined with other tools such as OCR and integrated into visual AI systems.

It can help us with many things, including extraction of essential information from images and putting them into text documents or JSON. And in the future, it can revolutionize search engines, and streamline automated text translation or entire workflows of document processing and archiving.

Examples of OCR Software & ChatGPT Working Together

So, now that we can combine computer vision and advanced natural language processing, let’s take a look at how we can use this technology to our advantage.

Reading, Processing and Mining Invoices From PDFs

One of the typical examples of OCR software is reading the data from invoices, receipts, or contracts from image-only PDFs (or other documents). Imagine a part of invoices and receipts your accounting department accepts are physical printed documents. You could scan the document, and instead of opening it in Adobe Acrobat and doing manual data entry (which is still a standard procedure in many accounting departments today), you would let the automated OCR system handle the rest.

Scanned documents can be automatically sent to the API from both computers and mobile phones. The visual AI needs only a few hundred milliseconds to process an image. Then you will get textual data with the desired structure in JSON or another format. You can easily integrate such technology into accounting systems and internal infrastructures to streamline invoice processing, payments or SKU numbers monitoring.

Trading Card Identifying & Reading Powered by AI

In recent years, the collector community for trading cards has grown significantly. This has been accompanied by the emergence of specialized collector websites, comparison platforms, and community forums. And with the increasing number of both cards and their collectors, there has been a parallel demand for automating the recognition and cataloguing collectibles from images.

Ximilar has been developing AI-powered solutions for some of the biggest collector websites on the market. And adding an OCR system was an ideal solution for data extraction from both cards and their graded slabs.

Automatic Recognition of Collectibles

Ximilar built an AI system for the detection, recognition and grading of collectibles. Check it out!

Read more

We developed an OCR system that extracts all text characters from both the card and its slab in the image. Then GPT processes these texts and provides structured information. For instance, the name of the player, the card, its grade and name of grading company, or labels from PSA.

Needless to say, we are pretty big fans of collectible cards ourselves. So we’ve been enjoying working on AI not only for sports cards but also for trading card games. We recently developed several solutions tuned specifically for the most popular trading card games such as Pokémon, Magic the Gathering or YuGiOh! and have been adding new features and games constantly. Do you like the idea of trading card recognition automation? See how it works in our public demo.

How Can I Use the OCR & GPT API On My Images or PDFs?

Our OCR software is publicly available via an online REST API. This is how you can use it:

1. Log into Ximilar App

   • Get your free API TOKEN to connect to API – Once you sign up to Ximilar App, you will get a free API token, which allows your authentication. The API documentation is here to help you with the basic setup. You can connect it with any programming language and any platform like iOS or Android. We provide a simple Python SDK for calling the API.

   • You can also try the service directly in the App under Computer Vision Platform.

2. For simple text extraction from your image, call the endpoint read.

   https://api.ximilar.com/ocr/v2/read

3. For text extraction from an image and its post-processing with GPT, use the endpoint read_gpt. To get the results in a deserved structure, you will need to specify the prompt query along with your input images in the API request, and the system will return the results immediately.

   https://api.ximilar.com/ocr/v2/read_gpt

4. The output is JSON with an ‘_ocr’ field. This dictionary contains texts that represent a list of polygons that encapsulate detected words and sentences in images. The full_text field contains all strings concatenated together. The API is returning also the language name (“lang_name”) and language code (“lang”; ISO 639-1). Here is an example:

   {
     "_url": "__URL_PATH_TO_IMAGE__
     "_ocr": {
        "texts": [
          {
             "polygon": [[53.0,76.0],[116.0,76.0],[116.0,94.0],[53.0,94.0]],
             "text": "MICKEY MANTLE",
             "prob": 0.9978849291801453
          },
          ...
        ],
        "full_text": "MICKEY MANTLE 1st Base Yankees",
        "lang_name": "english",
        "lang_code": "en
     }
   }

   Our OCR engine supports several alphabets (Latin, Chinese, Korean, Japanese and Cyrillic) and languages (English, German, Chinese, …).

Integrate the Combination of OCR and ChatGPT In Your System

All our solutions, including the combination of OCR & GPT, are available via API. Therefore, they can be easily integrated into your system, website, app, or infrastructure.

Here are some examples of up-to-date solutions that can easily be built on our platform and automate your workflows:

• Detection, recognition & text extraction system – You can let the users of your website or app upload images of collectibles and get relevant information about them immediately. Once they take an image of the item, our system detects its position (and can mark it with a bounding box). Then, it recognizes their features (e.g., name of the card, collectible coin or comic book), extracts texts with OCR and you will get text data for your website (e.g., in a table format).

• Card grade reading system – If your users upload images of graded cards or other collectibles, our system can detect everything including the grades and labels on the slabs in a matter of milliseconds.

• Comic book recognition & search engine – You can extract all texts from each image of a comic book and automatically match it to your database for cataloguing.

• Giving your collection or database of collectibles order – Imagine you have a website featuring a rich collection of collectible items, getting images from various sources and comparing their prices. The metadata can be quite inconsistent amongst source websites, or be absent in the case of user-generated content. AI can recognize, match, find and extract information from images based purely on computer vision and independent of any kind of metadata.

Let’s Build Your Solution

If you would like to learn more about how you can automate the workflows in your company, I recommend browsing our page All Solutions, where we briefly explained each solution. You can also check out pages such as Visual AI for Collectibles, or contact us right away to discuss your unique use case. If you’d like to learn more about how we work on customer projects step by step, go to How it Works.

Ximilar’s computer vision platform enables you to develop AI-powered systems for image recognition, visual quality control, and more without knowledge of coding or machine learning. You can combine them as you wish and upgrade any of them anytime.

Don’t forget to visit the free public demo to see how the basic services work. Your custom solution can be assembled from many individual services. This modular structure enables us to upgrade or change any piece anytime, while you save your money and time.

The post When OCR Meets ChatGPT AI in One API appeared first on Ximilar: Visual AI for Business.

How API Credits Work

Imagine you upload a training image, create a recognition label, or send an image for recognition in our App. Every time you perform an operation like this, you send a request to our server using API. This request is called an API call.

To keep track of API calls and their requirements, each type of call corresponds to a certain number of API credits. Generally, all calls sending image data to our servers cost some API credits. The full list of operations with their API credit values is available in our documentation.

Your Monthly API Credits

Every user of the Ximilar App is provided with a monthly supply of API credits, depending on their pricing plan. This supply is renewed every month on the day they made the purchase of their plan. For example, if you purchase a Business plan on April 15th, your monthly supply will be restored on the 15th day of every consequent month.

The users with the Free pricing plan are provided with a monthly supply of API credits as well. Whether you use a paid or free plan, the unused API credits from your monthly supply are not transferred to the following month and expire.

Introducing API Credit Packs

Ximilar App users can now buy an unlimited number of API credits aside from their monthly supply, in the form of API credit packs. This option is available for all pricing plans, including the Free plan.

There are two major benefits of the API credit packs. First, credits from the packs are used only when your monthly supply of credits runs out. In this example, the user with the Business plan has already used all API credits from his monthly supply and the system automatically switched to using the API credit pack. On April 15th, his monthly credit balance will be renewed, and the system will switch back to the monthly supply.

Second, API credit packs have no expiration. Therefore, their balance passes to the next month. You can buy as many credit packs as you need. The credits will add up in the lower API credit bar.

Typical Uses for API Credit Packs

The credit packs cover both expected and unexpected system loads. There are more ways and situations in which they can help or serve as safety nets.

Get Your System Ready

Our users generally pick their pricing plan based on regular traffic on their websites. However, the initial service setup is more demanding, and it costs a lot of extra credits. In this case, you wouldn’t want to upgrade your pricing plan for the short period of higher workloads and then downgrade back to the plan suiting your long-term needs.

One-Time System Loads

As you could see in the example with a Business plan user, the number of API credits in the credit pack bar was twice as high as his monthly credit supply. It is common for our users to use an above-average number of credits from time to time – typically when they are expecting higher system loads than usual. For example, uploading more products and images, or adding a brand new collection, would mean withdrawing your monthly credit supply too soon. In such cases, API credit packs provide a cost-effective solution.

Safety Net in a Case of Higher Traffic

The credit packs also cover the situations of unpredicted system loads caused by third parties. For example, when your website is visited and the system is used by an unexpected number of customers in a short period.

This way, the credit packs provide a sort of safety net to make sure no service outages will occur on your side due to the sudden exhaustion of credits.

What if I Upgrade or Downgrade My Plan?

You can always upgrade or downgrade your pricing plan. When this happens, the credits from your previous plan’s monthly supply will add up to the monthly supply of your new plan. They will remain in the bar till the end of your old monthly subscription and will be used first. In addition, you can purchase as many credit packs as you need, and the credits from the packs will be used after both of your monthly supplies are exhausted.

Do you have any questions? We’re more than happy to talk.

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New Label Types: Categories & Tags

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