SAVRN
Search Contact SAVRN

Open-weight model · Image to text

meiki.text.detect.v0

by Rtr46 rtr46/meiki.text.detect.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.

Parameters
Context
Weights80.8 MB
Licenselgpl-3.0
AccessOpen weights
Monthly Downloads50.5k

Model Card

By Rtr46, published under lgpl-3.0, revision a9cffa4f60cb.

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.detect.v0.1 is an update to meiki.text.detect.v0 (see below): - meiki.text.detect.v0.1 is a new state-of-the-art, open weight text detection model for video games beating text detection models like PaddleOCR - while it is still based on D-FINE detector, it uses mobilenet v4 small as backbone instead of hgnet v2 - v0.1 models increase focus on video game text detection and are limited to 64 detected boxes, increasing efficency for this use case (making them less suitable for manga text detection out of the box)…

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.detect.v0.1

meiki.text.detect.v0.1 is an update to meiki.text.detect.v0 (see below): - meiki.text.detect.v0.1 is a new state-of-the-art, open weight text detection model for video games beating text detection models like PaddleOCR - while it is still based on D-FINE detector, it uses mobilenet v4 small as backbone instead of hgnet v2 - v0.1 comes in 2 variants: v0.1.960x544 and v0.1.320x192. unlike v0 both v0.1 variants share the same architecture, but are trained on different resolutions - v0.1 models increase focus on video game text detection and are limited to 64 detected boxes, increasing efficency for this use case (making them less suitable for manga text detection out of the box) - v0.1.960x544 and v0.1.320x192 have better accuracy and lower latency than small.v0 and tiny.v0 respectively

cpu gpu

meiki.text.detect.v0

experimental text detection models with focus on low latency. trained on japanese video games and manga.

model versions: - tiny: good for images with only few textlines (e.g. visual novels). ~30ms latency on CPU. ~3ms on GPU. - small: better for cases with many textlines (e.g. manga). ~70ms latency on CPU. ~7ms on GPU.

fine-tune of https://github.com/Peterande/D-FINE

examples

visual novel

small tiny

manga

small tiny

Identity and Version

Repository
rtr46/meiki.text.detect.v0
Publisher
Rtr46
Task
Image to text
Modality
Image and text
Library
tensorrt
Parameters
Not stated by the source
Languages
en, ja
Revision
a9cffa4f60cbf72ddb87edf19c6f98a01cd042e6
First published
2025-10-21
Last updated
2025-11-06

Files and Weights

10 files, 80.8 MB in total. The weights are 4 files totalling 80.8 MB in onnx.

Weights4 files · 80.8 MB
Configuration4 files · 20.2 KB
Documentation1 file · 2.6 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
meiki.text.detect.small.v0.onnxWeights41.6 MB 3b702a7e7c93
meiki.text.detect.tiny.v0.onnxWeights10.6 MB 2e03bb478db6
meiki.text.detect.v0.1.320x192.onnxWeights14.1 MB 8cdc5daa5c13
meiki.text.detect.v0.1.960x544.onnxWeights14.5 MB 40b6a0166677
inference.small.pyConfiguration6.1 KB
inference.tiny.pyConfiguration5.0 KB
inference.v0.1.320x192.pyConfiguration4.6 KB
inference.v0.1.960x544.pyConfiguration4.6 KB
README.mdDocumentation2.6 KB
.gitattributesRepository1.5 KB

License and Download

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

Released by Rtr46 through its official repository on Hugging Face.

Memory Requirements

PrecisionWeights in memory
As published80.8 MB

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

Questions About meiki.text.detect.v0

What license is meiki.text.detect.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