SmartResume - Intelligent Resume Parsing System

💻 Code&nbsp&nbsp | &nbsp&nbsp🤗 Model&nbsp&nbsp | &nbsp&nbsp🤖 Demo&nbsp&nbsp | &nbsp&nbsp📑 Technical Report

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Project Introduction

SmartResume is an layout‑aware resume parsing system. It ingests resumes in PDF, image and common Office formats, extracts clean text (OCR + PDF metadata), reconstructs reading order with layout detection, and leverages LLMs to convert content into structured fields such as basic info, education, and work experience.

demo

Quick Start

Requirements

• Python >= 3.9
• CUDA >= 11.0 (optional, for GPU acceleration)
• Memory >= 8GB
• Storage >= 10GB

Installation

1. Clone the repository

   git clone https://github.com/alibaba/SmartResume.git
   cd SmartResume

2. Create conda environment

   conda create -n resume_parsing python=3.9
   conda activate resume_parsing

3. Install dependencies

   pip install -e .

4. Configure environment

   # Copy configuration template
   cp configs/config.yaml.example configs/config.yaml
   # Edit configuration file and add API keys
   vim configs/config.yaml

Basic Usage

Method 1: Command Line Interface (Recommended)

# Parse single resume file
python scripts/start.py --file resume.pdf

# Specify extraction types
python scripts/start.py --file resume.pdf --extract_types basic_info work_experience education

Method 2: Python API

from smartresume import ResumeAnalyzer

# Initialize analyzer
analyzer = ResumeAnalyzer(init_ocr=True, init_llm=True)

# Parse resume
result = analyzer.pipeline(
    cv_path="resume.pdf",
    resume_id="resume_001",
    extract_types=["basic_info", "work_experience", "education"]
)

print(result)

Local Model Deployment

SmartResume now supports local model deployment using vLLM, reducing dependency on external APIs:

# Download Qwen-0.6B-resume model
python scripts/download_models.py

# Deploy model
bash scripts/start_vllm.sh

For detailed local model deployment guide, see LOCAL_MODELS.

Key Features

Benchmark Results

For detailed benchmark results, see Benchmark Results.

Configuration

For detailed configuration options, see the Configuration Guide.

License Information

This project is licensed under the LICENSE.

We plan to explore and replace them with models under more permissive licenses to enhance user-friendliness and flexibility.

Important Notice

Due to open-source compliance requirements, this codebase is a refactored version. The internal PDF parsing and OCR components cannot be published. We have replaced them with open-source alternatives, and some features may not be fully compatible with the original implementation.

TODO List

1. Avoid loading models on every call, support vLLM deployment - Optimize model loading mechanism to avoid repeated loading and support vLLM deployment calls
2. Watermarked and corrupted PDF parsing via OCR pipeline - Use OCR pipeline for parsing watermarked or corrupted PDF files
3. Simpler API interface - Simplify API calls to improve usability

Acknowledgments

• PDFplumber
• EasyOCR

Citation

@article{Zhu2025SmartResume,
  title={Layout-Aware Parsing Meets Efficient LLMs: A Unified, Scalable Framework for Resume Information Extraction and Evaluation},
  author={Fanwei Zhu and Jinke Yu and Zulong Chen and Ying Zhou and Junhao Ji and Zhibo Yang and Yuxue Zhang and Haoyuan Hu and Zhenghao Liu},
  journal={arXiv preprint arXiv:2510.09722},
  year={2025},
  url={https://arxiv.org/abs/2510.09722}
}

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