Initial commit: Invoice field extraction system using YOLO + OCR
Features: - Auto-labeling pipeline: CSV values -> PDF search -> YOLO annotations - Flexible date matching: year-month match, nearby date tolerance - PDF text extraction with PyMuPDF - OCR support for scanned documents (PaddleOCR) - YOLO training and inference pipeline - 7 field types: InvoiceNumber, InvoiceDate, InvoiceDueDate, OCR, Bankgiro, Plusgiro, Amount Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Yaojia Wang
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README.md
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README.md
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# Invoice Master POC v2
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自动账单信息提取系统 - 使用 YOLO + OCR 从 PDF 发票中提取结构化数据。
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## 运行环境
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> **重要**: 本项目需要在 **WSL (Windows Subsystem for Linux)** 环境下运行。
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### 系统要求
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- WSL 2 (Ubuntu 22.04 推荐)
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- Python 3.10+
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- **NVIDIA GPU + CUDA 12.x (强烈推荐)** - GPU 训练比 CPU 快 10-50 倍
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## 功能特点
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- **双模式 PDF 处理**: 支持文本层 PDF 和扫描图 PDF
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- **自动标注**: 利用已有 CSV 结构化数据自动生成 YOLO 训练数据
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- **字段检测**: 使用 YOLOv8 检测发票字段区域
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- **OCR 识别**: 使用 PaddleOCR 提取检测区域的文本
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- **智能匹配**: 支持多种格式规范化和上下文关键词增强
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## 支持的字段
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| 字段 | 说明 |
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|------|------|
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| InvoiceNumber | 发票号码 |
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| InvoiceDate | 发票日期 |
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| InvoiceDueDate | 到期日期 |
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| OCR | OCR 参考号 (瑞典) |
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| Bankgiro | Bankgiro 号码 |
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| Plusgiro | Plusgiro 号码 |
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| Amount | 金额 |
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## 安装 (WSL)
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### 1. 进入 WSL 环境
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```bash
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# 从 Windows 终端进入 WSL
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wsl
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# 进入项目目录 (Windows 路径映射到 /mnt/)
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cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2
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```
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### 2. 安装系统依赖
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```bash
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# 更新系统
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sudo apt update && sudo apt upgrade -y
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# 安装 Python 和必要工具
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sudo apt install -y python3.10 python3.10-venv python3-pip
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# 安装 OpenCV 依赖
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sudo apt install -y libgl1-mesa-glx libglib2.0-0 libsm6 libxrender1 libxext6
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```
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### 3. 创建虚拟环境并安装依赖
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```bash
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# 创建虚拟环境
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python3 -m venv venv
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source venv/bin/activate
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# 升级 pip
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pip install --upgrade pip
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# 安装依赖
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pip install -r requirements.txt
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# 或使用 pip install (开发模式)
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pip install -e .
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```
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### GPU 支持 (可选)
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```bash
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# 确保 WSL 已配置 CUDA
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nvidia-smi # 检查 GPU 是否可用
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# 安装 GPU 版本 PaddlePaddle
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pip install paddlepaddle-gpu
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# 或指定 CUDA 版本
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pip install paddlepaddle-gpu==2.5.2.post118 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
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```
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## 快速开始
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### 1. 准备数据
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```
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data/
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├── raw_pdfs/
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│ ├── {DocumentId}.pdf
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│ └── ...
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└── structured_data/
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└── invoices.csv
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```
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CSV 格式:
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```csv
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DocumentId,InvoiceDate,InvoiceNumber,InvoiceDueDate,OCR,Bankgiro,Plusgiro,Amount
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3be53fd7-...,2025-12-13,100017500321,2026-01-03,100017500321,53939484,,114
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```
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### 2. 自动标注
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```bash
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python -m src.cli.autolabel \
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--csv data/structured_data/invoices.csv \
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--pdf-dir data/raw_pdfs \
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--output data/dataset \
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--report reports/autolabel_report.jsonl
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```
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### 3. 训练模型
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> **重要**: 务必使用 GPU 进行训练！CPU 训练速度非常慢。
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```bash
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# GPU 训练 (强烈推荐)
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python -m src.cli.train \
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--data data/dataset/dataset.yaml \
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--model yolo11n.pt \
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--epochs 100 \
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--batch 16 \
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--device 0 # 使用 GPU
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# 验证 GPU 可用
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python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}, GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else None}')"
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```
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GPU vs CPU 训练时间对比 (100 epochs, 77 训练图片):
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- **GPU (RTX 5080)**: ~2 分钟
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- **CPU**: 30+ 分钟
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### 4. 推理
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```bash
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python -m src.cli.infer \
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--model runs/train/invoice_fields/weights/best.pt \
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--input path/to/invoice.pdf \
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--output result.json
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```
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## 输出示例
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```json
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{
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"DocumentId": "3be53fd7-d5ea-458c-a229-8d360b8ba6a9",
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"InvoiceNumber": "100017500321",
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"InvoiceDate": "2025-12-13",
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"InvoiceDueDate": "2026-01-03",
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"OCR": "100017500321",
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"Bankgiro": "5393-9484",
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"Plusgiro": null,
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"Amount": "114.00",
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"confidence": {
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"InvoiceNumber": 0.96,
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"InvoiceDate": 0.92,
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"Amount": 0.93
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}
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}
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```
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## 项目结构
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```
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invoice-master-poc-v2/
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├── src/
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│ ├── pdf/ # PDF 处理模块
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│ ├── ocr/ # OCR 提取模块
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│ ├── normalize/ # 字段规范化模块
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│ ├── matcher/ # 字段匹配模块
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│ ├── yolo/ # YOLO 标注生成
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│ ├── inference/ # 推理管道
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│ ├── data/ # 数据加载模块
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│ └── cli/ # 命令行工具
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├── configs/ # 配置文件
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├── data/ # 数据目录
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└── requirements.txt
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```
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## 开发优先级
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1. ✅ 文本层 PDF 自动标注
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2. ✅ 扫描图 OCR 自动标注
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3. 🔄 金额 / OCR / Bankgiro 三字段稳定
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4. ⏳ 日期、Plusgiro 扩展
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5. ⏳ 表格 items 处理
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## 配置
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编辑 `configs/default.yaml` 自定义:
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- PDF 渲染 DPI
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- OCR 语言
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- 匹配置信度阈值
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- 上下文关键词
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- 数据增强参数
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## API 使用
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```python
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from src.inference import InferencePipeline
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# 初始化
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pipeline = InferencePipeline(
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model_path='models/best.pt',
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confidence_threshold=0.5,
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ocr_lang='en'
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)
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# 处理 PDF
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result = pipeline.process_pdf('invoice.pdf')
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# 获取字段
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print(result.fields)
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print(result.confidence)
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```
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## 许可证
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MIT License
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