DeQA-Doc-Overall is a vision-language model for assessing the overall quality of document images. It provides a quality score from 1 (bad) to 5 (excellent) that reflects the general visual quality of scanned or photographed documents.

Model Family

This model is part of the DeQA-Doc family, which includes three specialized models:

Model	Description	HuggingFace
DeQA-Doc-Overall	Overall document quality (this model)	mapo80/DeQA-Doc-Overall
DeQA-Doc-Color	Color quality assessment	mapo80/DeQA-Doc-Color
DeQA-Doc-Sharpness	Sharpness/clarity assessment	mapo80/DeQA-Doc-Sharpness

Quick Start

python

import torch
from transformers import AutoModelForCausalLM
from PIL import Image

# Load the model
model = AutoModelForCausalLM.from_pretrained(
    "mapo80/DeQA-Doc-Overall",
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map="auto",
)

# Score an image
image = Image.open("document.jpg").convert("RGB")
score = model.score([image])
print(f"Overall Quality Score: {score.item():.2f} / 5.0")

Batch Processing

You can score multiple images at once:

python

images = [
    Image.open("doc1.jpg").convert("RGB"),
    Image.open("doc2.jpg").convert("RGB"),
    Image.open("doc3.jpg").convert("RGB"),
]

scores = model.score(images)
for i, score in enumerate(scores):
    print(f"Document {i+1}: {score.item():.2f} / 5.0")

Score Interpretation

Score Range	Quality Level	Description
4.5 - 5.0	Excellent	Perfect quality, no visible defects
3.5 - 4.5	Good	Minor imperfections, highly readable
2.5 - 3.5	Fair	Noticeable issues but still usable
1.5 - 2.5	Poor	Significant quality problems
1.0 - 1.5	Bad	Severe degradation, hard to read

Model Architecture

Base Model: mPLUG-Owl2 (LLaMA2-7B + ViT-L Vision Encoder)
Vision Encoder: CLIP ViT-L/14 (1024 visual tokens via Visual Abstractor)
Language Model: LLaMA2-7B
Training: Full fine-tuning on document quality datasets
Input Resolution: Images are resized to 448x448 (with aspect ratio preservation)

Technical Details

Property	Value
Model Size	~16 GB (float16)
Parameters	~7.2B
Input	RGB images (any resolution)
Output	Quality score (1.0 - 5.0)
Inference	~2-3 seconds per image on A100

Hardware Requirements

Setup	VRAM Required	Recommended
Full precision (fp32)	~32 GB	A100, H100
Half precision (fp16)	~16 GB	A100, A40, RTX 4090
With CPU offload	~8 GB GPU + RAM	RTX 3090, RTX 4080

GPU Inference (Recommended)

python

model = AutoModelForCausalLM.from_pretrained(
    "mapo80/DeQA-Doc-Overall",
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map="auto",
)

CPU Offload (Lower VRAM)

python

model = AutoModelForCausalLM.from_pretrained(
    "mapo80/DeQA-Doc-Overall",
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map="auto",
    offload_folder="/tmp/offload",
)

Installation

pip install torch transformers accelerate pillow sentencepiece protobuf

Note: Use transformers>=4.36.0 for best compatibility.

Use Cases

Document Scanning QA: Automatically flag low-quality scans for re-scanning
Archive Digitization: Prioritize documents needing restoration
OCR Preprocessing: Filter images likely to produce poor OCR results
Document Management: Sort and categorize documents by quality
Quality Control: Automated quality checks in document processing pipelines

Example: Quality-Based Filtering

python

import torch
from transformers import AutoModelForCausalLM
from PIL import Image
from pathlib import Path

model = AutoModelForCausalLM.from_pretrained(
    "mapo80/DeQA-Doc-Overall",
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map="auto",
)

# Filter documents by quality
def filter_by_quality(image_paths, min_score=3.0):
    good_docs = []
    bad_docs = []

    for path in image_paths:
        img = Image.open(path).convert("RGB")
        score = model.score([img]).item()

        if score >= min_score:
            good_docs.append((path, score))
        else:
            bad_docs.append((path, score))

    return good_docs, bad_docs

# Usage
docs = list(Path("documents/").glob("*.jpg"))
good, bad = filter_by_quality(docs, min_score=3.5)

print(f"Good quality: {len(good)} documents")
print(f"Need review: {len(bad)} documents")

Limitations

Optimized for document images (forms, letters, reports, etc.)
May not perform well on natural photos or artistic images
Requires GPU with sufficient VRAM for efficient inference
Score is subjective and based on training data distribution

Credits & Attribution

This model is based on the DeQA-Doc project by Junjie Gao et al., which won the Championship in the VQualA 2025 DIQA (Document Image Quality Assessment) Challenge.

Original Repository: https://github.com/Junjie-Gao19/DeQA-Doc

All credit for the research, training methodology, and model architecture goes to the original authors.

Citation

If you use this model in your research, please cite the original paper:

bibtex

@inproceedings{deqadoc,
  title={{DeQA-Doc}: Adapting {DeQA-Score} to Document Image Quality Assessment},
  author={Gao, Junjie and Liu, Runze and Peng, Yingzhe and Yang, Shujian and Zhang, Jin and Yang, Kai and You, Zhiyuan},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop},
  year={2025},
}

ArXiv: https://arxiv.org/abs/2507.12796

License

Apache 2.0

Related Models

DeQA-Doc-Color - Color quality assessment
DeQA-Doc-Sharpness - Sharpness assessment