Instructions to use ONTHEIT/BizOnAI-OCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ONTHEIT/BizOnAI-OCR with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="ONTHEIT/BizOnAI-OCR")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ONTHEIT/BizOnAI-OCR")
model = AutoModelForImageTextToText.from_pretrained("ONTHEIT/BizOnAI-OCR")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ONTHEIT/BizOnAI-OCR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ONTHEIT/BizOnAI-OCR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ONTHEIT/BizOnAI-OCR",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ONTHEIT/BizOnAI-OCR

SGLang

How to use ONTHEIT/BizOnAI-OCR with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ONTHEIT/BizOnAI-OCR" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ONTHEIT/BizOnAI-OCR",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ONTHEIT/BizOnAI-OCR" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ONTHEIT/BizOnAI-OCR",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ONTHEIT/BizOnAI-OCR with Docker Model Runner:
```
docker model run hf.co/ONTHEIT/BizOnAI-OCR
```

BizOnAI-OCR

A Korean-optimized OCR model for industrial document processing developed by ONTHEIT.

Built on Qwen3-VL-8B, BizOnAI-OCR is tuned for real-world Korean documents — contracts, medical records, financial forms, government paperwork, and more. It handles Korean-specific layouts, mixed Korean/English/Chinese text, decorative spacing, vertical tables, and complex forms that trip up general-purpose OCR models.

Highlights

Korean-first: fine-tuned on Korean industrial documents
Bilingual: remains competitive on English OCR benchmarks
Markdown output: structured output with tables, headings, and formatting preserved
vLLM-ready: served efficiently via vLLM (OpenAI-compatible API) or plain transformers

Benchmark Results

KDoc-OCRBench (Korean)

Evaluated on KDoc-OCRBench — 14,738 unit-test-style assertions across 804 Korean industrial PDFs.

Model	Baseline	Header/Footer	Long Text	Table	Overall
BizOnAI-OCR	98.1	88.8	77.0	68.0	83.0
olmOCR v0.2.0	99.9	88.5	73.7	53.4	78.9
PaddleOCR-VL	100.0	90.5	65.7	57.6	78.4
DeepSeek OCR	99.8	88.3	66.0	53.1	76.8
GLM OCR	99.9	96.3	41.8	31.0	67.2

olmOCR-bench (English)

Evaluated on olmOCR-bench by Allen AI.

Model	ArXiv	Old Scans Math	Tables	Old Scans	Headers & Footers	Multi Column	Long Tiny Text	Base	Overall
BizOnAI-OCR	84.0	74.2	89.7	47.3	92.4	80.2	92.5	98.7	82.4
PaddleOCR-VL	85.7	71.0	84.1	37.8	97.0	79.9	85.7	98.5	80.0
olmOCR v0.2.0	78.8	77.5	71.9	45.4	94.2	78.6	81.4	99.8	78.5
DeepSeek OCR	77.2	73.6	80.2	33.3	96.1	66.4	79.4	99.8	75.7

Quick Start

Full installation instructions, CLI tools, and the benchmark pipeline are in the GitHub repository.

Serve with vLLM (recommended)

python -m vllm.entrypoints.openai.api_server \
    --model ONTHEIT/BizOnAI-OCR \
    --port 8000 \
    --dtype bfloat16 \
    --max-model-len 16384 \
    --trust-remote-code \
    --served-model-name bizonai-ocr

Python CLI

git clone https://github.com/ONTHEIT-AI/BizOnAI-OCR.git
cd BizOnAI-OCR && uv venv && source .venv/bin/activate
uv pip install -e .
bizonai-ocr input.pdf ./output

Tested Environment

Python 3.12, vllm==0.11.0, transformers==4.57.6, torch==2.8.0+cu128 (CUDA 12.8)

Credits

Qwen3-VL by Alibaba
olmocr by Allen AI
olmOCR-bench by Allen AI
vLLM

License

This repository is released under the Apache2.0 and OpenRAIL. See the LICENSE file for details.

Downloads last month: 393

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for ONTHEIT/BizOnAI-OCR

Base model

Qwen/Qwen3-VL-8B-Instruct

Finetuned

(274)

this model