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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.
Fonte do modelo
Descrição da fonte
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.
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 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.
Fontes
1 fonteVerificado 10 de set.
Artefatos de modelo
2 artefatosTrechos de fonte
2 trechos| cpu | gpu |
|---|---|
| accuracy_vs_cpu_latency | accuracy_vs_gpu_latency |
please refer to this demo inference script: https://huggingface.co/rtr46/meiki.txt.recognition.v0/blob/main/inference.py
input
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]}
]
}
--- license: lgpl-3.0 tags: - onnx - text-recognition - textline_recognition - computer-vision - japanese - ocr - OCR library_name: tensorrt language: - en - ja pipeline_tag: image-to-text --- --- **Official GitHub Repository:** [meikiocr](https://github.com/rtr46/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 ``` ```json { "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",...
Source context: 84247 downloads · 7 likes · Pipeline image-to-text · Library tensorrt · Repo rtr46/meiki.txt.recognition.v0