meiki.txt.recognition.v0 EU AI Act Compliance Profile

Official GitHub Repository: meikiocr

This model is a core component of the meikiocr pipeline. For the full implementation, command-line script, and documentation, please see the official GitHub repository.

meiki.text.recognition.v0

pareto-optimal text recognition model. trained on japanese video games.

meiki.text.recognition achieves state-of-the-art text recognition accuracy as well as latency by redefining "text recognition" as "character detection". the model is a fine-tune of https://github.com/Peterande/D-FINE object detecor combined with a mobilenetv4 CNN backbone. to my knowledge there is no existing, open weight text recognition model with a better accuracy/latency tradeoff for japanese text recognition.

intended use and constraints

• it is specifically trained on japanese video games, therefore performance may vary outside of this use case
• input needs to be resized and padded to be 960x32px
• outputs detection results as characters + bbox + confidence. check the inference.py script for a suggested post processing algorithms
• can detect up to 48 characters
• only works on horizontal text

benchmarks

| cpu | gpu | |:---:|:---:| | | |

how to use

please refer to this demo inference script: https://huggingface.co/rtr46/meiki.txt.recognition.v0/blob/main/inference.py

examples

{
	"text":"その一つの実情が、第一層の一画、空気には黴臭さと変に饐えた甘",
	"chars":[
		{"char":"そ","bbox":[2,0,33,32]},
		{"char":"の","bbox":[33,0,65,32]},
		{"char":"一","bbox":[65,0,96,32]},
		{"char":"つ","bbox":[97,0,1

Your obligations as a meiki.txt.recognition.v0 deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Library