ViTOCR Base
This repository contains a pure Transformer-based checkpoint for Khmer OCR. Images are patch-embedded and encoded by a Transformer encoder, then decoded autoregressively.
Model source
- Kind
- Other
- File size
- 640 MB
- Version
- vRevision 468778f
- Updated
- Sep 25, 2026
- Downloads
- 6
- Tasks
- Captioning
- Source
- Hugging Face
Source description
ViTOCR
This repository contains a pure Transformer-based checkpoint for Khmer OCR. Images are patch-embedded and encoded by a Transformer encoder, then decoded autoregressively.
Installation
pip install onnxruntime pillow torch torchvision numpy
Get the inference script
Download from this model repo:
curl -L -o onnx_inference.py https://huggingface.co/metythorn/ViTOCR-base/resolve/main/onnx_inference.py
Or copy onnx_inference.py from the repository files into your project directory.
Usage
from onnx_inference import ONNXPredictor
predictor = ONNXPredictor(
model_path="model.onnx",
config_path="config.json",
providers=["CPUExecutionProvider"], # or include CUDAExecutionProvider if available
)
result = predictor.predict("sample_image.png")
print("Predicted text:", result)
Sources
1 source
Sources
1 sourceVerified Sep 11
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- language: - km license: apache-2.0 tags: - ocr - transformer - vision pipeline_tag: image-to-text --- # ViTOCR This repository contains a pure Transformer-based checkpoint for Khmer OCR. Images are patch-embedded and encoded by a Transformer encoder, then decoded autoregressively. ## Installation ```python pip install onnxruntime pillow torch torchvision numpy ``` ## Get the inference script Download from this model repo: ```bash curl -L -o onnx_inference.py https://huggingface.co/metythorn/ViTOCR-base/resolve/main/onnx_inference.py ``` Or copy `onnx_inference.py` from the repository files into your project directory. ## Usage ```python from onnx_inference import ONNXPredictor predictor = ONNXPredictor( model_path="model.onnx", config_path="config.json", providers=["CPUExecutionProvider"], # or include CUDAExecutionProvider if available ) result = predictor.predict("sample_image.png") print("Predicted text:", result) ```
Source context: 6 downloads · 0 likes · Pipeline image-to-text · Repo metythorn/ViTOCR-base
