---
license: apache-2.0
tags:
  - onnx
  - paddleocr
  - image-quality-assessment
  - ocr
  - feature-extraction
  - document-quality
  - vision-encoder
library_name: onnxruntime
pipeline_tag: feature-extraction
datasets:
  - Aslan-mingye/OCR-Quality
language:
  - zh
  - en
  - multilingual
metrics:
  - spearmanr
  - pearsonr
  - accuracy
---

# PaddleOCR-VL Layer-12 Feature Extractor (ONNX)

**Truncated PaddleOCR-VL vision encoder — 1152D intermediate features for document image quality assessment.**

Layer 12 of the PaddleOCR-VL vision encoder captures mid-level visual patterns (texture, edge structure, compression artifacts, noise patterns) that are highly predictive of OCR readability. This ONNX model extracts those features with **zero PyTorch dependency** — just `onnxruntime` + `numpy` + `Pillow`.

## Why Layer 12?

From a sensitivity analysis across all 27 encoder layers:

| Degradation | Best Layer | Spearman ρ | Notes |
|---|---|---|---|
| Gaussian Noise | layer_00 | **0.96** | Shallow layers detect fine noise |
| JPEG Compression | layer_08 | **0.86** | Mid layers sense blocking artifacts |
| Gaussian Blur | layer_08 | 0.66 | Moderate response |
| Downscale | pooled | 0.65 | Resolution loss detectable |

**Layer 12** was chosen because:
- **Highest intra/inter-class distance ratio** (0.29) — features cluster by quality level, not degradation type
- Cross-resolution consistency **>0.99** — same image at 224² vs 728² produces nearly identical features
- Good balance: deeper than noise-sensitive layers, shallower than semantic layers

## Domain & Positioning: 这个模型做什么、不做什么

### 一句话定位

**这是一个"OCR 场景的文档图片质量特征提取器"，不是通用图片质量打分器，也不是 OCR 识别器。**

它的核心假设是：*如果一张图在 PaddleOCR-VL 的中间层特征空间里和清晰参考图很接近，那这张图大概率能被 OCR 正确识别。*

### 使用场景

| 场景 | 是否适合 | 说明 |
|---|---|---|
| 📄 **文档上传质量校验** | ✅ 核心场景 | "这张扫描件够清晰吗？需要让用户重拍吗？" |
| 🏦 **票据/证件 OCR 前置过滤** | ✅ 推荐 | 在送入 OCR 引擎前筛掉模糊/噪声过大的图片 |
| 📸 **拍照文档质量评分** | ✅ 适合 | 模糊、噪声、JPEG 压缩 → 有强信号 |
| 🌐 **通用图片美学评分** | ❌ 不适合 | 模型不理解"构图"、"色彩搭配"、"主体突出" |
| 🌃 **自然场景图片质量** | ⚠️ 有限 | 对噪声/模糊有响应，但对过曝/偏色无感知 |
| 🔤 **OCR 文字识别** | ❌ 做不了 | 这是特征提取器，不是 OCR 引擎。请用完整版 PaddleOCR-VL |
| 🎯 **文字语义理解** | ❌ 做不了 | 深层语义信息在 layer 20+，layer 12 以纹理/结构信息为主 |

### 与同类模型的关键区别

#### 1. vs 完整 PaddleOCR-VL（端到端 OCR 模型）

| | 本模型 (Layer-12 ONNX) | 完整 PaddleOCR-VL |
|---|---|---|
| **任务** | 图片质量特征提取 | 端到端 OCR（检测+识别） |
| **输出** | 1152D 特征向量 | 文字检测框 + 文本内容 |
| **推理速度** | ~100ms CPU | ~2-5s GPU |
| **依赖** | onnxruntime only | PyTorch + transformers |
| **部署** | 任意 ONNX 运行时（含移动端/边缘） | 需要 GPU 服务器 |
| **理解内容** | 不懂文字内容，只看纹理质量 | 完整理解文档内容 |

**选择指南**：如果你需要的是"这张图能读吗"的答案 → 用本模型提取特征 + 轻量回归器。如果你需要"图里写了什么" → 直接用完整 PaddleOCR-VL。

#### 2. vs DINOv3（通用视觉特征提取器）

| | 本模型 (Layer-12) | DINOv3 (ViT-S/16) |
|---|---|---|
| **训练数据** | PaddleOCR-VL 图文数据（文档为主） | 大规模自然图片（ImageNet/LAION 级别） |
| **特征偏好** | 纹理、边缘、压缩伪影（文档质量相关） | 语义类别、物体形状、全局布局 |
| **文档噪声敏感度** | ⭐⭐⭐⭐⭐ 极高（ρ=0.96 for noise） | ⭐⭐ 中等（对噪声有响应但不如专用模型） |
| **亮度/对比度感知** | ⭐ 弱（盲区） | ⭐⭐ 中等 |
| **特征维度** | 1152D（单层） | 384D (ViT-S) ~ 1536D (ViT-L) |

**关键差异**：DINOv3 是"通才"，擅长判断"这是什么物体"；本模型是"文档专才"，擅长判断"这文档还能读吗"。在文档质量任务上，本模型的特征 + 轻量回归器（84K 参数）达到 ρ=0.74，优于直接对 DINOv3 特征做 XGBoost。

实测对比（合成文档质量数据集，12 种退化）：

| 特征源 + 回归器 | Spearman ρ | R² |
|---|---|---|
| **PaddleOCR Layer-12 + Two-Tower MLP** | **0.74** | 0.44 |
| PaddleOCR Layer-12 + XGBoost | 0.60 | 0.35 |
| CV 传统特征 only + XGBoost | 0.66 | 0.33 |
| DINOv3 ViT-S + XGBoost | ~0.50 | ~0.25 |

#### 3. vs Qwen3-VL-Embedding / CLIP Embedding（通用 VLM 特征）

| | 本模型 (Layer-12) | Qwen3-VL-Embedding / CLIP |
|---|---|---|
| **特征来源** | 视觉编码器中间层（layer 12/27） | 视觉+文本对齐后的池化输出 |
| **语义层次** | 中层纹理/结构（edges, textures, artifacts） | 高层语义（"这是一张包含表格的文档"） |
| **跨模态** | 纯视觉，无文本对齐 | 视觉-文本对齐（CLIP 训练目标） |
| **质量退化感知** | ⭐⭐⭐⭐⭐ 天然敏感 | ⭐⭐ "模糊的文档"和"清晰的文档"在语义空间可能很近 |
| **相似度计算** | 余弦距离 ≈ 退化程度 | 余弦相似度 ≈ 语义相似度 |

**核心差异**：CLIP/Qwen3-VL 的 embedding 回答"这两张图内容像不像"；本模型的特征回答"这张图和清晰版差多远"。对于文档质量任务，你**不想要**语义相似度——模糊的身份证和清晰的身份证在语义上是同一张证，但在质量维度上区别巨大。

#### 4. vs Q-ReAlign（通用 VLM 质量评分）

Q-ReAlign 是直接用 VLM 做质量评分的方案（将质量分数映射到离散 token 做软分类）。

| | 本模型 (Layer-12) | Q-ReAlign |
|---|---|---|
| **方法论** | 特征提取 + 下游回归器 | 端到端 VLM 质量打分 |
| **文档质量表现** | ρ=0.74 | ρ ≈ -0.60（对 blur/JPEG 评分反向） |
| **原因** | 特征空间天然反映退化程度 | 训练数据是自然照片美学评分，不适用于文档 |
| **优势** | 文档场景专用，轻量部署 | 通用场景，零样本即可用 |

**选择指南**：Q-ReAlign 适合自然照片（"这张风景照好看吗"），不适合文档（"这张扫描件能 OCR 吗"）。

#### 5. vs 传统 CV 质量指标（Laplacian / FFT / Canny）

| | 本模型 (Layer-12) | 传统 CV 指标 |
|---|---|---|
| **噪声检测 ρ** | **0.96** | ~0.30 (Laplacian 对噪声不敏感) |
| **模糊检测 ρ** | 0.72 | **~0.85** (Laplacian 天然适合) |
| **压缩伪影 ρ** | **0.86** | ~0.20 (无对应指标) |
| **亮度异常** | 0.05 (盲区) | **~0.80** (直方图直接捕获) |
| **可解释性** | 黑盒向量 | ✅ 每个指标有物理含义 |
| **部署成本** | ONNX Runtime ~100ms | 零成本，纯数学运算 <1ms |

**结论**：两者互补。传统 CV 覆盖亮度/对比度（模型的盲区），模型覆盖噪声/压缩（CV 的盲区）。最佳方案是**双塔融合**：本模型 1152D + CV 6D → 联合回归器。

### 模型选型速查表

| 你的需求 | 推荐方案 |
|---|---|
| 文档上传前质量检查 | **本模型** (feature extractor) + 轻量 Two-Tower 回归器 |
| 通用图片质量打分（非文档） | Q-ReAlign / CLIP-IQA / MUSIQ |
| 端到端 OCR 识别 | 完整 PaddleOCR-VL / PaddleOCR Server |
| 零依赖快速模糊检测 | 传统 CV (Laplacian variance) |
| 图片语义搜索/相似度 | CLIP / Qwen3-VL-Embedding / DINOv3 |
| 移动端文档质量 | **本模型 ONNX** (支持 ONNX Runtime 全平台) |

## Model Architecture

```
Input Image (any size)
    │
    ▼
PaddleOCRVLImageProcessor (smart_resize + normalize + patchify)
    │
    ▼
Patch Embedding + Position Encoding
    │
    ▼
Transformer Encoder Layers 0 → 12  ← TRUNCATED HERE (original: 27 layers)
    │
    ▼
Mean Pooling across patches
    │
    ▼
1152-D Feature Vector
```

| Property | Value |
|---|---|
| Parameters | 237M (905 MB fp32 weights) |
| Input | `pixel_values`: (1, N, 3, 14, 14) float32, `position_ids`: (1, 1) int64 |
| Output | `layer_12_pooled`: (1, 1152) float32 |
| Dynamic axes | num_patches (varies with input resolution) |
| Opset | 18 |
| Files | `model.onnx` (851 KB graph) + `model.onnx_data` (902 MB weights) |

## Installation

```bash
pip install onnxruntime numpy Pillow opencv-python
```

No PyTorch, no transformers, no CUDA required. CPU inference is ~50-200ms per image.

## Quick Start

```python
from inference.onnx_inference import Layer12ONNXExtractor
from PIL import Image, ImageFilter

# Load model
extractor = Layer12ONNXExtractor("model.onnx")

# Extract features
img = Image.open("document.jpg").convert("RGB")
features = extractor.extract(img)  # → np.ndarray shape (1152,)

# Quality via distance from pristine reference
pristine = Image.open("pristine.jpg").convert("RGB")
blurred = img.filter(ImageFilter.GaussianBlur(radius=5))
quality = extractor.quality_score(blurred, reference=pristine)
# → 0.0 (heavily degraded) ~ 1.0 (pristine)
```

See [`inference/example.py`](inference/example.py) for a complete walkthrough.

## Feature Quality Benchmarks

Run with: `python benchmark/run_benchmark.py`

### 1. Degradation Sensitivity (Spearman ρ)

Correlation between feature distance and degradation severity across 12 degradation types × 7 levels × 5 image types:

| Degradation | Spearman ρ | Monotonicity | Sensitivity |
|---|---|---|---|
| Gaussian Noise | **0.96** | 0.98 | Excellent |
| JPEG Compression | **0.86** | 0.97 | Excellent |
| Gaussian Blur | 0.72 | 0.95 | Good |
| Motion Blur | 0.68 | 0.93 | Good |
| Median Blur | 0.65 | 0.92 | Good |
| Downscale | 0.65 | 0.90 | Good |
| ISO Noise | 0.60 | 0.88 | Fair |
| Salt & Pepper | 0.55 | 0.85 | Fair |
| Rotation | 0.30 | 0.60 | Weak |
| Brightness | 0.05 | 0.52 | Blind spot |
| Contrast | 0.05 | 0.50 | Blind spot |

**Mean |ρ| = 0.55** across all degradations.

### 2. Paired Ranking Accuracy

Given a pristine reference and two degraded copies at different severity levels, can the model correctly rank which is worse?

**Overall: 89%** across 200 random pairs.

### 3. Cross-Resolution Consistency

Same content at different resolutions → same features? Cosine similarity between features extracted at 224² vs 728²:

**Mean: 0.997** — near-perfect consistency thanks to PaddleOCR-VL's `smart_resize`.

## Use Cases

1. **Document upload quality gate** — reject blurry/noisy scans before OCR
2. **Image preprocessing quality monitor** — detect when enhancement pipeline degrades
3. **Reference-based quality scoring** — compare against a known-good template
4. **Feature backbone for quality regression** — use 1152D features as input to a lightweight quality regressor (e.g., Two-Tower MLP, XGBoost)

## Limitations

- **Blind to brightness/contrast issues** — these don't affect the feature space. Complement with traditional CV metrics (Laplacian variance, histogram stats).
- **Geometric degradations (rotation) have weak signal** — the model uses position encoding, but rotated text still looks like text to mid-layer features.
- **Not a standalone quality scorer** — this is a feature extractor. You need a downstream head (trained regressor, reference comparison, or CV complement) for final quality scores.
- **Original PaddleOCR-VL base model** — the vision encoder is from PaddleOCR-VL, which is optimized for Chinese + English document OCR.

## Recommended Quality Pipeline

For a robust document quality system, use a **Two-Tower fusion**:

```
PaddleOCR-VL Layer 12 (this model)     OpenCV traditional metrics
         │                                      │
   PCA → 128D                             6D vector
   Deep Tower MLP                         CV Tower MLP
         │                                      │
         └──────── concat (80D) ───────────────┘
                         │
                   Shared Head
                         │
                  quality_score [0, 1]
```

This approach achieves **ρ = 0.74** on held-out data (vs 0.60 for XGBoost on raw features).

## Open-Source Benchmarks for Evaluation

If you want to benchmark your quality model against published work:

### Direct: OCR Quality Assessment

| Dataset | Size | Focus | Link |
|---|---|---|---|
| **OCR-Quality** | 1,000 images | Human-annotated OCR quality (4-level) | [Aslan-mingye/OCR-Quality](https://huggingface.co/datasets/Aslan-mingye/OCR-Quality) |
| **DocPTBench** | 1,300+ photos | Photographed document quality (geometric + photometric) | [Topdu/DocPTBench](https://github.com/Topdu/DocPTBench) |

### General Image Quality Assessment (IQA)

| Dataset | Size | Focus | Link |
|---|---|---|---|
| **LIVE** | 779 images | 5 distortion types, DMOS labels | [UT Austin LIVE](https://live.ece.utexas.edu/research/quality/) |
| **CSIQ** | 866 images | 6 distortion types, DMOS labels | [CSIQ](https://qualinet.github.io/databases/) |
| **TID2013** | 3,000 images | 24 distortion types, MOS labels | [TID2013](https://qualinet.github.io/databases/) |
| **KADID-10k** | 10,125 images | 25 distortion types × 5 levels | [KADID-10k](https://database.mmsp-kn.de/kadid-10k-database.html) |
| **KonIQ-10k** | 10,073 images | In-the-wild quality, MOS labels | [KonIQ-10k](https://database.mmsp-kn.de/koniq-10k-database.html) |

### OCR Recognition Benchmarks

| Dataset | Size | Focus | Link |
|---|---|---|---|
| **GlotOCR Bench** | 158 scripts | Multilingual OCR with degraded variants | HuggingFace (eval license) |
| **Misraj-DocOCR** | 400 images | Arabic document OCR (WER/CER) | Open-source |
| **OCRGenBench** | 1,060 samples | OCR generation quality | [PeirongZhang/OCRGenBench](https://huggingface.co/datasets/PeirongZhang/OCRGenBench) |

**Recommended starting point**: [OCR-Quality](https://huggingface.co/datasets/Aslan-mingye/OCR-Quality) — it's the only dataset that directly measures OCR quality assessment, with human annotations and per-page MOS scores. Our benchmark script includes a loader for it.

## Files

```
paddleocr-quality-onnx/
├── README.md                        # This file
├── .gitattributes                   # HF LFS config
├── requirements.txt                 # Python dependencies
├── model.onnx           # ONNX model (~0.8 MB)
├── inference/
│   ├── __init__.py
│   ├── preprocessing.py             # Standalone image preprocessing (no torch)
│   ├── onnx_inference.py            # Layer12ONNXExtractor class
│   └── example.py                   # Complete usage example
└── benchmark/
    ├── run_benchmark.py             # Degradation sensitivity, ranking, resolution
    └── results/                     # Benchmark output
```

## Citation

If you use this model in your research:

```bibtex
@software{paddleocr-quality-onnx,
  title = {PaddleOCR-VL Layer-12 Feature Extractor (ONNX)},
  year = {2025},
  note = {Truncated PaddleOCR-VL vision encoder for document image quality assessment},
  url = {https://huggingface.co/[your-username]/paddleocr-quality-onnx},
}
```

The original PaddleOCR-VL model is from PaddlePaddle. This is a derived work using only the vision encoder (layers 0-12), exported to ONNX for lightweight deployment.

## License

This model is derived from PaddleOCR-VL, which is released under Apache 2.0. This ONNX export and accompanying code are also Apache 2.0.