The 0.9B OCR Model That Beats Gemini? (GLM-OCR) | Benchmarks + Demo | Live Coding + Q&A (Mar 19th)

GLM-OCR packs just 0.9B parameters — a 0.4B CogViT visual encoder and a 0.5B GLM language decoder — yet it tops OmniDocBench V1.5 at 94.62, approaching Gemini-level performance. A Multi-Token Prediction mechanism lets it decode multiple tokens per step, keeping latency low enough for edge deployment and production workloads. In this stream I first benchmark GLM-OCR across 8 diverse datasets — captchas, LaTeX equations, receipts, date stamps, jersey numbers, container serials, tire codes, and license plates — to test its limits on real-world images. Then I build a complete smart parking management system that chains license plate detection, OC-SORT multi-object tracking, and GLM-OCR into a pipeline that reads plates automatically as vehicles enter a lot. Both Colab notebooks are linked below so you can follow along. Resources: 📓 How to Perform OCR with GLM-OCR: https://colab.research.google.com/git... 📓 Smart Parking Management with GLM-OCR: https://colab.research.google.com/git... 📄 GLM-OCR Paper: https://arxiv.org/abs/2603.10910 🤗 GLM-OCR on HuggingFace: https://huggingface.co/zai-org/GLM-OCR Stay updated with the projects I'm working on at https://github.com/roboflow and https://github.com/SkalskiP! ⭐