license: cc-by-nc-4.0
inference: false
base_model: naver-clova-ix/donut-base
tags:
- donut
- image-to-text
- vision
model-index:
- name: donut-receipts-extract
results:
- task:
type: image-to-text
name: Image to text
metrics:
- type: loss
value: 0.326069
- type: accuracy
value: 0.895219
name: Accuracy
- type: cer
value: 0.158358
name: CER
- type: wer
value: 1.673989
name: WER
- type: edit distance
value: 0.145293
name: Edit_distance
metrics:
- cer
- wer
- accuracy
datasets:
- AdamCodd/donut-receipts
pipeline_tag: image-to-text
Donut-receipts-extract
Donut model was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.
=== V2 ===
This model has been retrained on an improved version of the AdamCodd/donut-receipts dataset (deduplicated, manually corrected). The new license for the V2 model is cc-by-nc-4.0. For commercial use rights, please contact me (adamcoddml@gmail.com). Meanwhile, the V1 model remains available under the MIT license (under v1 branch).
It achieves the following results on the evaluation set:
- Loss: 0.326069
- Edit distance: 0.145293
- CER: 0.158358
- WER: 1.673989
- Mean accuracy: 0.895219
- F1: 0.977897
The task_prompt has been changed to <s_receipt> for the V2 (previously <s_cord-v2> for V1). Two new keys <s_svc> and <s_discount> have been added, <s_telephone> has been renamed to <s_phone>.
The V2 performs way better than the V1 as it has been trained on twice the resolution for the receipts, using a better dataset. Despite that, it's not perfect due to a lack of diverse receipts (the training dataset is still ~1100 receipts); for a future version, that will be the main focus.
=== V1 ====
This model is a finetune of the donut base model on the AdamCodd/donut-receipts dataset. Its purpose is to efficiently extract text from receipts.
It achieves the following results on the evaluation set:
- Loss: 0.498843
- Edit distance: 0.198315
- CER: 0.213929
- WER: 7.634032
- Mean accuracy: 0.843472
Model description
Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder.
How to use
import torch
import re
from PIL import Image
from transformers import DonutProcessor, VisionEncoderDecoderModel
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
processor = DonutProcessor.from_pretrained("AdamCodd/donut-receipts-extract")
model = VisionEncoderDecoderModel.from_pretrained("AdamCodd/donut-receipts-extract")
model.to(device)
def load_and_preprocess_image(image_path: str, processor):
"""
Load an image and preprocess it for the model.
"""
image = Image.open(image_path).convert("RGB")
pixel_values = processor(image, return_tensors="pt").pixel_values
return pixel_values
def generate_text_from_image(model, image_path: str, processor, device):
"""
Generate text from an image using the trained model.
"""
# Load and preprocess the image
pixel_values = load_and_preprocess_image(image_path, processor)
pixel_values = pixel_values.to(device)
# Generate output using model
model.eval()
with torch.no_grad():
task_prompt = "<s_receipt>" # <s_cord-v2> for v1
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
decoder_input_ids = decoder_input_ids.to(device)
generated_outputs = model.generate(
pixel_values,
decoder_input_ids=decoder_input_ids,
max_length=model.decoder.config.max_position_embeddings,
pad_token_id=processor.tokenizer.pad_token_id,
eos_token_id=processor.tokenizer.eos_token_id,
early_stopping=True,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
return_dict_in_generate=True
)
# Decode generated output
decoded_text = processor.batch_decode(generated_outputs.sequences)[0]
decoded_text = decoded_text.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
decoded_text = re.sub(r"<.*?>", "", decoded_text, count=1).strip() # remove first task start token
decoded_text = processor.token2json(decoded_text)
return decoded_text
# Example usage
image_path = "path_to_your_image" # Replace with your image path
extracted_text = generate_text_from_image(model, image_path, processor, device)
print("Extracted Text:", extracted_text)
Refer to the documentation for more code examples.
Intended uses & limitations
This fine-tuned model is specifically designed for extracting text from receipts and may not perform optimally on other types of documents. The dataset used is still suboptimal (numerous errors are still there) so this model will need to be retrained at a later date to improve its performance.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 35
- weight_decay: 0.01
Framework versions
- Transformers 4.36.2
- Datasets 2.16.1
- Tokenizers 0.15.0
- Evaluate 0.4.1
If you want to support me, you can here.
BibTeX entry and citation info
@article{DBLP:journals/corr/abs-2111-15664,
author = {Geewook Kim and
Teakgyu Hong and
Moonbin Yim and
Jinyoung Park and
Jinyeong Yim and
Wonseok Hwang and
Sangdoo Yun and
Dongyoon Han and
Seunghyun Park},
title = {Donut: Document Understanding Transformer without {OCR}},
journal = {CoRR},
volume = {abs/2111.15664},
year = {2021},
url = {https://arxiv.org/abs/2111.15664},
eprinttype = {arXiv},
eprint = {2111.15664},
timestamp = {Thu, 02 Dec 2021 10:50:44 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}