GLM-OCR is a multimodal optical character recognition model that performs document understanding and text extraction from complex layouts.
The tool addresses the challenge of accurately extracting text and structured information from diverse document types including tables, formulas, and code-heavy layouts. It builds on the GLM-V encoder-decoder architecture and introduces Multi-Token Prediction loss alongside stable full-task reinforcement learning to improve training efficiency and generalization. The system combines a CogViT visual encoder with a lightweight cross-modal connector and a GLM language decoder, paired with a two-stage pipeline that performs layout analysis followed by parallel recognition.
Developers should choose this tool if they need robust OCR performance across varied real-world document types rather than simple text extraction. It suits production scenarios requiring high accuracy on complex tables, formulas, and challenging layouts. The model's 0.9B parameter count makes it suitable for deployment in high-concurrency services and edge environments where inference latency and compute cost matter. The tool provides multiple deployment options through vLLM, SGLang, and Ollama, and offers an SDK with simple installation and one-line invocation for integration into existing pipelines.
The project maintains active development with recent additions of an agent-friendly Skill mode requiring only API key configuration and no GPU or YAML setup, alongside a fine-tuning guide based on LLaMA-Factory. The codebase is fully open-sourced with comprehensive documentation and a published technical report supporting the implementation. Community engagement occurs through WeChat and Discord channels, indicating ongoing user support and discussion.
