SAVRN
Search Contact SAVRN

Open-weight model · Image to text

meiki.txt.recognition.v0

by Rtr46 rtr46/meiki.txt.recognition.v0

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

Parameters
Context
Weights31.5 MB
Licenselgpl-3.0
AccessOpen weights
Monthly Downloads99.3k

Model Card

By Rtr46, published under lgpl-3.0, revision a28cf5874dc2.

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. 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. - it is specifically trained on japanese…

Read Rtr46's full model card

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

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
{
    "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":[97,0,128,32]},
        {"char":"の","bbox":[129,0,160,32]},
        {"char":"実","bbox":[161,0,193,32]},
        {"char":"情","bbox":[192,0,224,32]},
        {"char":"が","bbox":[225,0,256,32]},
        {"char":"、","bbox":[258,0,288,32]},
        {"char":"第","bbox":[288,0,319,32]},
        {"char":"一","bbox":[321,0,352,32]},
        {"char":"層","bbox":[352,0,384,32]},
        {"char":"の","bbox":[384,0,415,32]},
        {"char":"一","bbox":[416,0,448,32]},
        {"char":"画","bbox":[448,0,479,32]},
        {"char":"、","bbox":[481,0,512,32]},
        {"char":"空","bbox":[513,0,544,32]},
        {"char":"気","bbox":[544,0,575,32]},
        {"char":"に","bbox":[576,0,608,32]},
        {"char":"は","bbox":[609,0,640,32]},
        {"char":"黴","bbox":[640,0,672,32]},
        {"char":"臭","bbox":[672,0,704,32]},
        {"char":"さ","bbox":[705,0,737,32]},
        {"char":"と","bbox":[738,0,767,32]},
        {"char":"変","bbox":[769,0,800,32]},
        {"char":"に","bbox":[800,0,832,32]},
        {"char":"饐","bbox":[833,0,864,32]},
        {"char":"え","bbox":[864,0,895,32]},
        {"char":"た","bbox":[897,0,928,32]},
        {"char":"甘","bbox":[929,0,960,32]}
    ]
}

releases

  • 21.02.2026: i released a new version of the checkpoint that should improve accuracy. if you want to use the old one for some reason use this revision: ddd06176a4da56fba082293dbe9898d4e5998af2

Identity and Version

Repository
rtr46/meiki.txt.recognition.v0
Publisher
Rtr46
Task
Image to text
Modality
Image and text
Library
tensorrt
Parameters
Not stated by the source
Languages
en, ja
Revision
a28cf5874dc2438ebb1c86336be26bcec51e3375
First published
2025-11-03
Last updated
2026-02-24

Files and Weights

6 files, 31.5 MB in total. The weights are 2 files totalling 31.5 MB in onnx.

Weights2 files · 31.5 MB
Configuration1 file · 5.1 KB
Documentation1 file · 3.6 KB
Other1 file · 33 B
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
meiki.text.rec.v0.960x32.onnxWeights18.6 MB 3e96bc772fbe
meiki.text.rec.v0.vertical.32x480.onnxWeights12.9 MB 2c2a83a23bc3
inference.pyConfiguration5.1 KB
README.mdDocumentation3.6 KB
requirements.txtOther33 B
.gitattributesRepository1.5 KB

License and Download

License
lgpl-3.0
Access
Open weights, no gate
Download size
31.5 MB
Download from Rtr46

Released by Rtr46 through its official repository on Hugging Face.

Memory Requirements

PrecisionWeights in memory
As published31.5 MB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About meiki.txt.recognition.v0

What license is meiki.txt.recognition.v0 released under?

lgpl-3.0, as its publisher declares it. Read the license text before commercial use.

Similar Models

captioning pretrained on COCO dataset - base architecture (with ViT base backbone). Authors from the paper write in the abstract: Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the…

Open weights bsd-3-clause 512 tokens transformers

Model · Image to text

manga-ocr-base

Maciej Budyś

Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses Vision Encoder Decoder framework. Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality text recognition, robust against various scenarios specific to manga: - both vertical and horizontal text - text with furigana - text overlaid on images - wide variety of fonts and font styles - low quality images Code is available here.

Open weights apache-2.0 transformers

Model · Image to text

PP-OCRv5_server_det

PaddlePaddle

PP-OCRv5serverdet is one of the PP-OCRv5det series, the latest generation of text detection models developed by the PaddleOCR team. Designed for high-performance applications, it supports the detection of text in diverse scenarios—including handwriting, vertical, rotated, and curved text—across multiple languages such as Simplified Chinese, Traditional Chinese, English, and Japanese. Key features include robust handling of complex layouts, varying text sizes, and challenging backgrounds, making it suitable for practical applications like document analysis, license plate recognition, and scene text detection. The key accuracy metrics are as follow: Please refer to the following commands to…

Open weights apache-2.0 PaddleOCR

Model · Image to text

UVDoc

PaddlePaddle

The main purpose of text image correction is to carry out geometric transformation on the image to correct the document distortion, inclination, perspective deformation and other problems in the image, so that the subsequent text recognition can be more accurate. Please refer to the following commands to install PaddlePaddle using pip: For details about PaddlePaddle installation, please refer to the PaddlePaddle official website. Install the latest version of the PaddleOCR inference package from PyPI: You can quickly experience the functionality with a single command: You can also integrate the model inference of the TextImageUnwarping module into your project. Before running the following…

Open weights apache-2.0 PaddleOCR

Model · Image to text

en_PP-OCRv5_mobile_rec

PaddlePaddle

enPP-OCRv5mobilerec is one of the PP-OCRv5rec that are the latest generation text line recognition models developed by PaddleOCR team. It aims to efficiently and accurately support the recognition of English. The key accuracy metrics are as follow: Note: If any character (including punctuation) in a line was incorrect, the entire line was marked as wrong. This ensures higher accuracy in practical applications. Please refer to the following commands to install PaddlePaddle using pip: For details about PaddlePaddle installation, please refer to the PaddlePaddle official website. Install the latest version of the PaddleOCR inference package from PyPI: You can quickly experience the…

Open weights apache-2.0 PaddleOCR

Model · Image to text

PP-LCNet_x1_0_doc_ori

PaddlePaddle

The Document Image Orientation Classification Module is primarily designed to distinguish the orientation of document images and correct them through post-processing. During processes such as document scanning or ID photo capturing, the device might be rotated to achieve clearer images, resulting in images with various orientations. Standard OCR pipelines may not handle these images effectively. By leveraging image classification techniques, the orientation of documents or IDs containing text regions can be pre-determined and adjusted, thereby improving the accuracy of OCR processing. The key accuracy metrics are as follow: Please refer to the following commands to install PaddlePaddle…

Open weights apache-2.0 PaddleOCR