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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 and inference toolchain, offering simple installation, one-line invocation, and smooth integration into existing production pipelines.
For speed, we compared different OCR methods under identical hardware and testing conditions (single replica, single concurrency), evaluating their performance in parsing and exporting Markdown files from both image and PDF inputs. Results show GLM-OCR achieves a throughput of 1.86 pages/second for PDF documents and 0.67 images/second for images, significantly outperforming comparable models.
or using docker with:
or build it from source with:
Ollama will automatically use image file path when an image is dragged into the terminal:
GLM-OCR currently supports two types of prompt scenarios:
⚠️ Note: When using information extraction, the output must strictly adhere to the defined JSON schema to ensure downstream processing compatibility.
We provide an easy-to-use SDK for using GLM-OCR more efficiently and conveniently. please check our github to get more detail.
This project is inspired by the excellent work of the following projects and communities:
The GLM-OCR model is released under the MIT License.
The complete OCR pipeline integrates 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.
pip install -U vllm --extra-index-url https://wheels.vllm.ai/nightlydocker pull vllm/vllm-openai:nightlypip install git+https://github.com/huggingface/transformers.gitvllm serve zai-org/GLM-OCR --allowed-local-media-path / --port 8080docker pull lmsysorg/sglang:devpip install git+https://github.com/sgl-project/sglang.git#subdirectory=pythonpip install git+https://github.com/huggingface/transformers.gitpython -m sglang.launch_server --model zai-org/GLM-OCR --port 8080ollama run glm-ocrollama run glm-ocr Text Recognition: ./image.pngpip install git+https://github.com/huggingface/transformers.gitfrom transformers import AutoProcessor, AutoModelForImageTextToTextimport torch
MODEL_PATH = "zai-org/GLM-OCR"messages = [ { "role": "user", "content": [ { "type": "image", "url": "test_image.png" }, { "type": "text", "text": "Text Recognition:" } ], }]processor = AutoProcessor.from_pretrained(MODEL_PATH)model = AutoModelForImageTextToText.from_pretrained( pretrained_model_name_or_path=MODEL_PATH, torch_dtype="auto", device_map="auto",)inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(model.device)inputs.pop("token_type_ids", None)generated_ids = model.generate(**inputs, max_new_tokens=8192)output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)print(output_text){ "text": "Text Recognition:", "formula": "Formula Recognition:", "table": "Table Recognition:"}请按下列JSON格式输出图中信息:{ "id_number": "", "last_name": "", "first_name": "", "date_of_birth": "", "address": { "street": "", "city": "", "state": "", "zip_code": "" }, "dates": { "issue_date": "", "expiration_date": "" }, "sex": ""}