Instructions to use lucky-verma/driver-license-reader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lucky-verma/driver-license-reader with Transformers:

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

pipe = pipeline("image-text-to-text", model="lucky-verma/driver-license-reader")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("lucky-verma/driver-license-reader")
model = AutoModelForMultimodalLM.from_pretrained("lucky-verma/driver-license-reader")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lucky-verma/driver-license-reader with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lucky-verma/driver-license-reader"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lucky-verma/driver-license-reader",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lucky-verma/driver-license-reader

SGLang

How to use lucky-verma/driver-license-reader 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 "lucky-verma/driver-license-reader" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lucky-verma/driver-license-reader",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "lucky-verma/driver-license-reader" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lucky-verma/driver-license-reader",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lucky-verma/driver-license-reader with Docker Model Runner:
```
docker model run hf.co/lucky-verma/driver-license-reader
```

Driver License Reader

Donut-based image-to-JSON extraction for driver's license documents.

driver license OCR alternative | ID card parsing | KYC document extraction | visual document understanding

Open the demo Space

What it does

This model is a fine-tuned Donut VisionEncoderDecoderModel for extracting structured fields from driver's license images without a separate OCR pipeline. It converts an input image into JSON-like fields such as:

name
state
date
dob
person

The model is intended for demos, prototyping, and research around document AI workflows. It is not a production identity-verification system.

Public-safety notes

Do not upload real driver's licenses unless you have permission and a lawful basis to process the data.
Prefer synthetic, redacted, or consented images for testing.
Outputs can be wrong or incomplete. Use human review before relying on extracted identity fields.
The repository keeps the safer model.safetensors weight file and does not require loading Pickle weights.

Quick start

import re
import torch
from PIL import Image
from transformers import DonutProcessor, VisionEncoderDecoderModel

model_id = "lucky-verma/driver-license-reader"
processor = DonutProcessor.from_pretrained(model_id)
model = VisionEncoderDecoderModel.from_pretrained(model_id)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device).eval()

image = Image.open("redacted_or_synthetic_license.jpg").convert("RGB")
pixel_values = processor(image, return_tensors="pt").pixel_values.to(device)

task_prompt = "<s_cord-v2>"
decoder_input_ids = processor.tokenizer(
    task_prompt,
    add_special_tokens=False,
    return_tensors="pt",
)["input_ids"].to(device)

with torch.inference_mode():
    outputs = model.generate(
        pixel_values,
        decoder_input_ids=decoder_input_ids,
        max_length=model.decoder.config.max_position_embeddings,
        early_stopping=True,
        pad_token_id=processor.tokenizer.pad_token_id,
        eos_token_id=processor.tokenizer.eos_token_id,
        use_cache=True,
        num_beams=1,
        bad_words_ids=[[processor.tokenizer.unk_token_id]],
        return_dict_in_generate=True,
    )

sequence = processor.batch_decode(outputs.sequences)[0]
sequence = sequence.replace(processor.tokenizer.eos_token, "")
sequence = sequence.replace(processor.tokenizer.pad_token, "")
sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()

print(processor.token2json(sequence))

Model details

Architecture: Donut / vision encoder-decoder
Base model: nielsr/donut-base
Format: model.safetensors
Input: image of a driver's license-like document
Output: structured JSON-style fields
Language: English

Limitations

This model was trained on a small driver's-license dataset and may fail on unseen layouts, glare, blur, occlusion, low-resolution scans, non-English documents, or non-US license formats. Treat the output as an extraction suggestion, not a verified identity record.

Downloads last month: 28

Safetensors

Model size

0.2B params

Tensor type

I64

F32

Inference Providers NEW

Image-Text-to-Text

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