## GLM-OCR [中文阅读](README_zh.md) <div align="center"> <img src=resources/logo.svg width="40%"/> </div> <p align="center"> 👋 Join our <a href="resources/WECHAT.md" target="_blank">WeChat</a> and <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community <br> 📖 Check out the GLM-OCR <a href="https://arxiv.org/abs/2603.10910" target="_blank">technical report</a> <br> 📍 Use GLM-OCR's <a href="https://docs.z.ai/guides/vlm/glm-ocr" target="_blank">API</a> </p> ### Model Introduction GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization. The model integrates the CogViT visual encoder pre-trained on large-scale image–text data, a lightweight cross-modal connector with efficient token downsampling, and a GLM-0.5B language decoder. Combined with a two-stage pipeline of layout analysis and parallel recognition based on PP-DocLayout-V3, GLM-OCR delivers robust and high-quality OCR performance across diverse document layouts. **Key Features** - **State-of-the-Art Performance**: Achieves a score of 94.62 on OmniDocBench V1.5, ranking #1 overall, and delivers state-of-the-art results across major document understanding benchmarks, including formula recognition, table recognition, and information extraction. - **Optimized for Real-World Scenarios**: Designed and optimized for practical business use cases, maintaining robust performance on complex tables, code-heavy documents, seals, and other challenging real-world layouts. - **Efficient Inference**: With only 0.9B parameters, GLM-OCR supports deployment via vLLM, SGLang, and Ollama, significantly reducing inference latency and compute cost, making it ideal for high-concurrency services and edge deployments. - **Easy to Use**: Fully open-sourced and equipped with a comprehensive [SDK](https://github.com/zai-org/GLM-OCR) and inference toolchain, offering simple installation, one-line invocation, and smooth integration into existing production pipelines. ### News & Updates - **[2026.3.12]** GLM-OCR SDK now supports agent-friendly Skill mode — just `pip install glmocr` + set API key, ready to use via CLI or Python with no GPU or YAML config needed. See: [GLM-OCR Skill](glmocr_skill/SKILL.md) - **[2026.3.12]** GLM-OCR Technical Report is now available. See: [GLM-OCR Technical Report](https://arxiv.org/abs/2603.10910) - **[2026.2.12]** Fine-tuning tutorial based on LLaMA-Factory is now available. See: [GLM-OCR Fine-tuning Guide](examples/finetune/README.md) ### Download Model | Model | Download Links | Precision | | ------- | --------------------------------------------------------------------------------------------------------------------------- | --------- | | GLM-OCR | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-OCR)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-OCR) | BF16 | ## GLM-OCR SDK We provide an SDK for using GLM-OCR more efficiently and conveniently. ### Install SDK Choose the lightest installation that matches your scenario: ```bash # Cloud / MaaS + local images / PDFs (fastest install) pip install glmocr # Self-hosted pipeline (layout detection) pip install "glmocr[selfhosted]" # Flask service support pip install "glmocr[server]" ``` Install from source for development: ```bash # Install from source git clone https://github.com/zai-org/glm-ocr.git cd glm-ocr uv venv --python 3.12 --seed && source .venv/bin/activate uv pip install -e . ``` ### Model Deployment Two ways to use GLM-OCR: #### Option 1: Zhipu MaaS API (Recommended for Quick Start) Use the hosted cloud API – no GPU needed. The cloud service runs the complete GLM-OCR pipeline internally, so the SDK simply forwards your request and returns the result. 1. Get an API key from https://open.bigmodel.cn 2. Configure `config.yaml`: ```yaml pipeline: maas: enabled: true # Enable MaaS mode api_key: your-api-key # Required ``` That's it! When `maas.enabled=true`, the SDK acts as a thin wrapper that: - Forwards your documents to the Zhipu cloud API - Returns the results directly (Markdown + JSON layout details) - No local processing, no GPU required Input note (MaaS): the upstream API accepts `file` as a URL or a `data:<mime>;base64,...` data URI. If you have raw base64 without the `data:` prefix, wrap it as a data URI (recommended). The SDK will auto-wrap local file paths / bytes / raw base64 into a data URI when calling MaaS. API documentation: https://docs.bigmodel.cn/cn/guide/models/vlm/glm-ocr #### Option 2: Self-host with vLLM / SGLang Deploy the GLM-OCR model locally for full control. The SDK provides the complete pipeline: layout detection, parallel region OCR, and result formatting. Install the self-hosted extra first: ```bash pip install "glmocr[selfhosted]" ``` ##### Using vLLM Install vLLM: ```bash docker pull vllm/vllm-openai:nightly ``` Or using with pip: ```bash pip install -U "vllm>=0.17.0" ``` Launch the service: ```bash pip install "transformers>=5.3.0" vllm serve zai-org/GLM-OCR --allowed-local-media-path / --port 8080 --speculative-config '{"method": "mtp", "num_speculative_tokens": 1}' --served-model-name glm-ocr ``` ##### Using SGLang Install SGLang: ```bash docker pull lmsysorg/sglang:dev ``` Or using with pip: ```bash pip install "sglang>=0.5.9" ``` Launch the service: ```bash pip install "transformers>=5.3.0" sglang serve --model zai-org/GLM-OCR --port 8080 --speculative-algorithm NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 --served-model-name glm-ocr ``` ##### Update Configuration After launching the service, configure `config.yaml`: ```yaml pipeline: maas: enabled: false # Disable MaaS mode (default) ocr_api: api_host: localhost # or your vLLM/SGLang server address api_port: 8080 ``` #### Option 3: Ollama/MLX For specialized deployment scenarios, see the detailed guides: - **[Apple Silicon with mlx-vlm](examples/mlx-deploy/README.md)** - Optimized for Apple Silicon Macs - **[Ollama Deployment](examples/ollama-deploy/README.md)** - Simple local deployment with Ollama ### SDK Usage Guide #### CLI ```bash # Parse a single image glmocr parse examples/source/code.png # Parse a directory glmocr parse examples/source/ # Set output directory glmocr parse examples/source/code.png --output ./results/ # Use a custom config glmocr parse examples/source/code.png --config my_config.yaml # Enable debug logging with profiling glmocr parse examples/source/code.png --log-level DEBUG ``` #### Python API ```python from glmocr import GlmOcr, parse # Simple function result = parse("image.png") result = parse(["img1.png", "img2.jpg"]) result = parse("https://example.com/image.png") result.save(output_dir="./results") # Note: a list is treated as pages of a single document. # Class-based API with GlmOcr() as parser: result = parser.parse("image.png") print(result.json_result) result.save() ``` #### Flask Service Install the optional server dependency first: ```bash pip install "glmocr[server]" ``` ```bash # Start service python -m glmocr.server # With debug logging python -m glmocr.server --log-level DEBUG # Call API curl -X POST http://localhost:5002/glmocr/parse \ -H "Content-Type: application/json" \ -d '{"images": ["./example/source/code.png"]}' ``` Semantics: - `images` can be a string or a list. - A list is treated as pages of a single document. - For multiple independent documents, call the endpoint multiple times (one document per request). ### Configuration Full configuration in `glmocr/config.yaml`: ```yaml # Server (for glmocr.server) server: host: "0.0.0.0" port: 5002 debug: false # Logging logging: level: INFO # DEBUG enables profiling # Pipeline pipeline: # OCR API connection ocr_api: api_host: localhost api_port: 8080 api_key: null # or set API_KEY env var connect_timeout: 300 request_timeout: 300 # Page loader settings page_loader: max_tokens: 16384 temperature: 0.01 image_format: JPEG min_pixels: 12544 max_pixels: 71372800 # Result formatting result_formatter: output_format: both # json, markdown, or both # Layout detection (optional) enable_layout: false ``` See [config.yaml](glmocr/config.yaml) for all options. ### Output Formats Here are two examples of output formats: - JSON ```json [[{ "index": 0, "label": "text", "content": "...", "bbox_2d": null }]] ``` - Markdown ```markdown # Document Title Body... | Table | Content | | ----- | ------- | | ... | ... | ``` ### Example of full pipeline you can run example code like： ```bash python examples/example.py ``` Output structure (one folder per input): - `result.json` – structured OCR result - `result.md` – Markdown result - `imgs/` – cropped image regions (when layout mode is enabled) ### Modular Architecture GLM-OCR uses composable modules for easy customization: | Component | Description | | --------------------- | -------------------------------------- | | `PageLoader` | Preprocessing and image encoding | | `OCRClient` | Calls the GLM-OCR model service | | `PPDocLayoutDetector` | PP-DocLayout layout detection | | `ResultFormatter` | Post-processing, outputs JSON/Markdown | You can extend the behavior by creating custom pipelines: ```python from glmocr.dataloader import PageLoader from glmocr.ocr_client import OCRClient from glmocr.postprocess import ResultFormatter class MyPipeline: def __init__(self, config): self.page_loader = PageLoader(config) self.ocr_client = OCRClient(config) self.formatter = ResultFormatter(config) def process(self, request_data): # Implement your own processing logic pass ``` ## Acknowledgement This project is inspired by the excellent work of the following projects and communities: - [PP-DocLayout-V3](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3) - [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) - [MinerU](https://github.com/opendatalab/MinerU) ## License The Code of this repo is under Apache License 2.0. The GLM-OCR model is released under the MIT License. The complete OCR pipeline integrates [PP-DocLayoutV3](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3) for document layout analysis, which is licensed under the Apache License 2.0. Users should comply with both licenses when using this project. ## Citation If you find GLM-OCR useful in your research, please cite our technical report: ```bibtex @misc{duan2026glmocrtechnicalreport, title={GLM-OCR Technical Report}, author={Shuaiqi Duan and Yadong Xue and Weihan Wang and Zhe Su and Huan Liu and Sheng Yang and Guobing Gan and Guo Wang and Zihan Wang and Shengdong Yan and Dexin Jin and Yuxuan Zhang and Guohong Wen and Yanfeng Wang and Yutao Zhang and Xiaohan Zhang and Wenyi Hong and Yukuo Cen and Da Yin and Bin Chen and Wenmeng Yu and Xiaotao Gu and Jie Tang}, year={2026}, eprint={2603.10910}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2603.10910}, } ```