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lucky-verma/
donut-demo

Name: donut-demo
Availability: InStock

transformers

pytorch

safetensors

vision-encoder-decoder

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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

python

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.

lucky-verma/donut-demo