Skip to main content

MK8DX Table Reader

A Python package to extract player names and scores from Mario Kart 8 Deluxe end-game tables using YOLO object detection and ONNX-based EasyOCR.

Installation

Option 1: Lightweight ONNX Version (Recommended)

# Install the package (without heavy PyTorch dependencies)
pip install mk8dx-table-reader

# Download ONNX EasyOCR models (~17 MB)
python download_easyocr_models.py

Total size: ~50 MB

Option 2: PyTorch Version (Fallback)

# Install with PyTorch-based EasyOCR
pip install mk8dx-table-reader[pytorch-ocr]

Total size: ~2 GB

Features

  • Extract player names from MK8DX end-game screenshots
  • Read scores and positions from tables
  • Uses YOLO for object detection
  • NEW: ONNX-based EasyOCR for lightweight inference (3-4x faster, 97% smaller)
  • Fallback to PyTorch EasyOCR if ONNX models not available
  • OCR powered by optimized ONNX models and Keras models

Requirements

Core Requirements

  • Python >= 3.11.11
  • OpenCV
  • ONNXRuntime
  • Ultralytics YOLO
  • NumPy
  • Pillow

Optional Requirements

  • EasyOCR (PyTorch-based, only if not using ONNX models)

Usage

from mk8dx_table_reader import Fullreader
import PIL

# Initialize reader (automatically uses ONNX if models are available)
reader = Fullreader()

# Load an image
img = PIL.Image.open("screenshot.png")

# Extract player names and scores
names, scores = reader.fullOCR(img)

print("Players:", names)
print("Scores:", scores)

ONNX EasyOCR Benefits

Feature PyTorch EasyOCR ONNX EasyOCR
Package Size ~2 GB ~17 MB
Inference Speed Baseline 3-4x faster
Memory Usage ~2 GB RAM ~50 MB RAM
Accuracy 100% >95%

See EASYOCR_ONNX_GUIDE.md for more details.

License

See LICENSE.md for details.

Author

Julien ABADIE - julien.abadie@etu.uca.fr

GitHub

https://github.com/PrizeWhipser/shortcat.tips

Metadata

Release files for mk8dx-table-reader 0.6.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mk8dx-table-reader 0.6.5
File Size Uploaded
mk8dx_table_reader-0.6.5.tar.gz 22.3 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for mk8dx-table-reader 0.6.5
File Interpreter ABI Platform
mk8dx_table_reader-0.6.5-py3-none-any.whl Python 3 none any Details

Total release size: 44.5 MB

Release files / mk8dx_table_reader-0.6.5.tar.gz

Download URL mk8dx_table_reader-0.6.5.tar.gz
Size 22.3 MB
Tags Source
SHA-256 checksum
How to use checksums
c79780ca2ae1d302eb9d36b74aacf30c693a15c4cf4d16040aebc34b3c180313
BLAKE2b-256 checksum
How to use checksums
cf1768779e9f78c6f617d1e1391bfde62648e904f073ff7ff747b915343d28f8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.13

Release files / mk8dx_table_reader-0.6.5-py3-none-any.whl

Download URL mk8dx_table_reader-0.6.5-py3-none-any.whl
Size 22.3 MB
Tags Python 3
SHA-256 checksum
How to use checksums
7e6c00d14d209ec6ae6eb8da8813269e1ddd3ebd5f9e1718ff2f830e40d69aef
BLAKE2b-256 checksum
How to use checksums
dba15b326e5d57fd408030a48e7c0c012b29e4cadfcb7ada0fc000735771ff38
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.13

Release history Release notifications | RSS feed

This release

0.6.5 This release

2 release files

0.6.4

2 release files

0.6.3

2 release files

0.6.2

2 release files

0.5.5

2 release files

0.5.4

2 release files

0.5.3

2 release files

0.5.2

2 release files

0.5.1

2 release files

0.5.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page