tags:
- ocr
- multilingual
- onnx
- scriptmoe
openocr-mlt ONNX Models
FP32 inference models for multilingual OCR with the openocr-mlt Python package. The bundle combines ScriptMoE autoregressive recognition with PP-OCRv6 medium text detection and provides whole-page OCR, text detection, and cropped text recognition.
Model details
| Property | Value |
|---|---|
| Recognition | ScriptMoE ONNX Prefill/Decode with KV caching |
| Detection | PP-OCRv6 medium ONNX |
| Precision | FP32 |
| Effective batch size | 1 |
| Maximum recognition length | 100 decoding steps |
The archive includes recognition graphs, shared weights, the character dictionary, the model manifest, and the detector.
Installation and use
Use Linux x86_64 with Python 3.10. Create a virtual environment and install the package.
python3.10 -m venv .venv-ocr
source .venv-ocr/bin/activate
python -m pip install openocr-mlt
The same installation supports CPU and NVIDIA CUDA on Linux x86_64. CUDA requires a compatible NVIDIA driver. The Python task API defaults to device="auto". Set device="cpu" or device="cuda:0" to select a device explicitly.
from openocr_mlt import OpenOCRMLT
with OpenOCRMLT(task="ocr") as ocr:
result = ocr(image_path="page.jpg")
print(result.text)
print(ocr.info["device"])
with OpenOCRMLT(task="det", device="cpu") as detector:
boxes = detector(image_path="page.jpg")
print([box.points for box in boxes])
with OpenOCRMLT(task="rec", device="cpu") as recognizer:
lines = recognizer(image_path="crop.jpg")
print([(line.text, line.score) for line in lines])
Replace the image paths with existing files. Use image= for a PIL image or a uint8 NumPy array. NumPy arrays use BGR by default. Specify color_order="RGB" for RGB arrays.
The first task call downloads and verifies the registered model bundle. ModelScope is the primary source, with Hugging Face and hf-mirror as fallbacks. The default model cache is $XDG_CACHE_HOME/openocr-mlt or ~/.cache/openocr-mlt.
First GPU use may require about 1.32 GB of additional CUDA 12 and cuDNN 9 libraries. The library preparation process displays download and verification progress. Compatible local libraries and verified caches can be reused. PyTorch is optional.
Model archive
| Property | Value |
|---|---|
| File | scriptmoe-medium-fp32-20261101-v1.tar.gz |
| Size | 213,794,128 bytes |
| SHA256 | ee43e7358066ddf1286f61983024dc407808e350b1dd57eb986523eaf9417ad3 |
For offline use, copy the archive to the target machine and import it with the installed package.
python -m openocr_mlt fetch-models \
--source ./scriptmoe-medium-fp32-20261101-v1.tar.gz \
--cache-dir ./models --offline
The command verifies the archive and model files before installing them in the cache. In the Python API, set cache_dir="./models", offline=True to use this model cache offline. CUDA use also requires compatible local runtime libraries or a previously prepared CUDA cache. Offline package installation requires local copies of the package dependencies.
Sources and licensing
Recognition derives from ScriptMoE, upstream revision 5dde99867aaee6b4af3e75a3020b225eab17ac53. Detection uses PP-OCRv6_medium_det_onnx.tar from Paddle's official model storage. Component attribution and licensing details are provided in THIRD_PARTY_NOTICES.md.
Package installation and API documentation are available on PyPI.