Trading Card Game card scanner using OCR (using Tesseract and OpenCV)
Currently only supports a sub set of Magic: The Gathering™ cards, but the design is generic enough to support any Trading Card Game that uses the card name as the main identifier of the card.
Inspired by card_scan, but using an OCR detection and doesn't try to find out where the card is. Instead, it shows a rectangle in the UI so the user can align the card.
My setup cost a total of AUD $10 and consists of:
- Kmart webcam (ID 05e3:0510 Genesys Logic, Inc)
- Lamp
- Webcam stand (old jar, wire)
| Date | Functionality | Comments | Videos |
|---|---|---|---|
| 2014-03-10 | Clipboard | This is the first demo of the app, the detection calculation wasn't correct as it included the UI rendering times. This mode is only useful if the system is always on, say, in a games shop selling singles. |  |
| 2014-03-11 | Export to deckbox.org | One of the output formats available is the deckbox.org csv format. From there, it is possible to manage the collection appropriately. A lot of the metadata on the card is still being worked on. Note that this detector captured both a split card (a hard one at that too) and a sleeved card. As long as the lighting is good, it will read the card, regardless of whether it is sleeved, in a binder or on its own. The main challenge is the alignment, since there is no card detector (yet). |  |
The system will guess the closest card, provided the poll threshold is high enough. It will only output anything if it has a good candidate, which means it will have very few false positives. Depending on the dictionary, the total guess space can be reduced as well. For example, if one is trying to detect in a space of 250 cards, then the dictionary should only have those and any 'random' card will either fit one of those or not get detected at all. There is no upper bound on how long the system takes to identify a card (though one might be added in the future). As it is now, it takes about 12s to detect a card name. Detection is highly dependant on image quality, lighting and camera focal distance. The current setup was optimised for 800x600 webcams, but the detection doesn't seem to depend on the resolution as much as it depends on image quality (nice focus, contrasting text).
- support scanning card by card at an acceptable speed (currently at about 12s)
- output to csv formats for importing into other applications
- call optional web service with the detected card
- import card list in other formats (csv, xml)
- design frame for scanning cards with an off-the-shelf webcam (OpenSCAD)
- design container for piling up cards in the scanner (OpenSCAD)
- design automated card switching mechanism (OpenSCAD + Arduino/RaspberriPi/WhateverWorks)
- opencv (debian package
python-opencv) - numpy
sudo pip install numpy - hunspell
git clone [email protected]:smathot/pyhunspell.git && cd pyhunspell/ && sudo ./setup.py install - xerox
sudo pip install xerox
On debian for example: sudo apt-get install make xclip tesseract
cards.xml with the cards you'd like to recognise. Current format included below.
<?xml version="1.0" encoding="UTF-8"?>
<whatevertagyoudlike>
<cards>
<card>
<name>CARD_NAME</name>
<set picURL="http://someURL?multiverseid=VENDOR_ID&someParam=X">SET_NAME</set>
</card>
...
</cards>
</whatevertagyoudlike>
- Webcam with reasonable resolution and lighting (tested 800x600)
- Optional rig to place the card in the best place for matching and prevent it from moving around
- Get a copy of
cards.xmland put it in./dict/ - Run
maketo generate the database and the dictionary
$ ./main.py -h
usage: main.py [-h] [--nobeep] [--clipboard]
[--batchfile BATCHFILE [BATCHFILE ...]] [-v int]
[-d DICTIONARY] [--min-suggestions int] [--min-length int]
Scan MTG cards using OCR (tesseract).
optional arguments:
-h, --help show this help message and exit
--nobeep disable beep when scan happens
--clipboard save card name to clipboard
--batchfile BATCHFILE [BATCHFILE ...]
save data to batch file
-v int, --verbosity int
verbosity level (0: quiet, 1: feedback, 2: debug)
-d DICTIONARY, --dictionary DICTIONARY
dictionary to use (e.g., dict/INN for dict/INN.dic +
dict/INN.aff)
--min-suggestions int
minimum number of suggestions polled (e.g., 5 means it
is considered a correct guess after 5 equal guesses)
--min-length int minimum length for a detected card name
--webcam int webcam number (usually, the biggest number is the one
you plugged in)
Using a cards.xml file with Magic: The Gathering™ card names
$ ./main.py -v 1Detected: Rapid Hybridization (12.84 secs)
Detected: Rapid Hybridization (10.45 secs)
Detected: Hands of Binding (12.42 secs)
Detected: Simic Charm (14.22 secs)
Detected: Riot Control (15.22 secs)
Detected: Mind Grind (9.07 secs)
Detected: Spark Trooper (6.47 secs)
Detected: Vorel of the Hull Clade (11.54 secs)
Detected: Varolz, the Scar-Striped (14.62 secs)
Detected: Teysa, Envoy of Ghosts (13.34 secs)
Detected: Kessig Wolf Run (12.89 secs)
Detected: Fireshrieker (18.82 secs)
Detected: Megantic Sliver (9.38 secs)
Detected: Elvish Mystic (14.29 secs)
Detected: Bane Alley Blackguard (9.46 secs)
Detected: Doom Blade (12.16 secs)
Detected: Bronzebeak Moa (9.38 secs)
Notice: The card names listed in this example are from Magic: The Gathering™, a trademark of Wizards of the Coast, Inc., a subsidiary of Hasbro, Inc. © 2014 Wizards. All Rights Reserved. This project is not affiliated with, endorsed, sponsored, or specifically approved by Wizards of the Coast LLC. Please contact me if there is a problem.
| Test date | Dictionary Size | Cards tested | Cards matched | Average T (s) | Max T (s) | Min T (s) | Median T (s) | Error rate |
|---|---|---|---|---|---|---|---|---|
| 2014-03-09 | 14123 | 57 | 51 | 22.8 | 103.5 | 7.39 | 16.19 | 10.5% |
| 2014-03-09 | 14123 | 94 | 90 | 20.8 | 100.49 | 6.04 | 12.82 | 4.26% |
| 2014-03-13 | 14123 | 102 | 99 | 7.96 | 75.48 | 1.27 | 5.28 | 2.94% |