NEW GLM OCR Update is INSANE!

New GLM OCR update is insane. A brand new OCR just dropped and it's completely free. This thing reads handwriting, tables, formulas, everything, and it beats models that are way bigger. I'm going to show you how to use it right now. Let's go. So, here's what just

happened. A company called GLM just released an OCR model that's only. 9 billion parameters. That's tiny, but it's beating models like Gemini Pro on benchmarks. This thing scored 94. 6 on Omnidocbench. That's the top score, number one. And it's open source with an MIT license, which means you can use it for anything. Commercial projects, personal stuff, whatever you want. Now, I know what you're thinking. Another OCR tool, right? We already have those. But this one's different. Most OCR tools just read text. They see letters and spit them out. That's it. GLM OCR actually understands what it's reading. It can handle complex tables, scientific formulas, handwritten notes, stamps and seals, codeheavy documents, multi- language scans, all the messy real world stuff that breaks normal OCR. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. Julian Goldie reads every comment, so make sure you comment below. Let me show you what this thing

can actually do. First up, text recognition. You feed it a scanned document and it pulls out all the text. Not just type text either. Handwritten notes, too. I'm talking about Dr. Handwriting that you can barely read yourself. It handles that. Uh, second, formula recognition. You got a scientific paper with latex formulas. It reads those and converts them to proper latte code. Third, table passing. Complex tables with merged cells and weird layouts, no problem. It converts them to HTML or markdown. Fourth, key information extraction. And here's the crazy part. It does all this with less than 1 billion parameters. Most models this good are 10 times bigger, 20 times bigger. This thing runs on your laptop. Low latency, low compute. You can deploy on edge devices, which means you can build OCR features into apps without needing a massive server farm. Let me

break down how to actually use this thing. There are three ways: command line, code integration, and API calls. I'll show you all three. First, command line. This is the easiest way to test out. You need Alarma installed. If you don't have it, go download it. It's free. Then you run one command. Alarm run glm OCR. That's it. Then you can feed it an image and ask it to extract text or pass a table or pull key information. One line. Done. Second code integration. You can use this in Python or JavaScript. They've got an SDK that makes it super simple. You import the library, load your image, call the OCR function, and you get structured output. JSON, Markdown, HTML, whatever format you need. This is perfect if you're building an app or automating workflows. You can process hundreds of documents in minutes. Third, API calls. If you want to deploy this as a service, you can use VLOM or escul. These are inference frameworks that let you serve the model at scale. High throughput, low latency. You can handle thousands of requests without breaking a sweat. Now, let me

show you some real world examples. Let's say you're a researcher. You're going through old scientific papers, tons of formulas, complex tables, you want to digitize all that data. Normally, you'd be retyping formulas by hand. That's slow and errorprone. With GLMocr, you scan the paper and it converts all the formulas to latex, all the tables to mark down. You can copy and paste that into your notes or database. Instant digitization. And if you want to learn how to build systems like this with AI tools like GMOCR, you need to join the AI profit boardroom. That's where I share the full workflows, SOPs, and automation strategies that are saving businesses hundreds of hours every month. We've got members scaling their companies with AI right now. They're automating customer service, data entry, content creation, all of it. And you can do the same. Links in the description.

Now, let's talk about what makes GLM OCR different from other OCR tools. Most OCR models are either really good at one thing or okay at everything. GLM OCR is good at everything. It's not just reading text. It's understanding context. It knows the difference between a table header and a table cell. It knows when something's a formula versus regular text. It knows when handwriting is part of a signature versus actual content. That level of understanding comes from the architecture. The model uses something called GLMV. That's a vision encoder combined with a language decoder. The vision part looks at the image and understands what it's seeing. The language part generates the output and there's a crossodal connector in between that links them together. This setup lets the model handle complex layouts, multicolumn documents, mixed language scans, rotated or skewed images, all the messy stuff that breaks simpler models. And because it's only. 9 billion parameters, it's fast, really fast. You can process a full page document in seconds. Compare that to larger models that take 10 or 20 seconds per page. When you're processing thousands of documents, that speed difference adds up. You go from hours to minutes. Here's another thing. The model is trained on real world data, not just clean textbook examples. It's seen messy scans, lowresolution images, faded text, handwritten notes with bad lighting, all the stuff you actually encounter when you're digitizing documents. That's why it's so robust. It doesn't break when you give it a crappy scan. It still extracts the data you need. Now, let me

talk about deployment because being able to use a model is one thing. Being able to deploy it in production is another. GLM OCR supports VLM, Slang, and Lama. These are all battle tested inference frameworks. VLM is great for high throughput. You can serve the model to thousands of users at once. SGLANG is optimized for low latency. You get responses in milliseconds. AMA is perfect for local deployment. You can run it on your own machine without needing cloud infrastructure. Now, here's something cool. You can chain GLM OCR with other AI tools. Let's say you extract text from a document. Then you feed that text into chat GPT or claude to summarize it. Or you extract table data and feed it into a data analysis tool out. Let me show you one more thing. Output formats. GLMCR can give you data in multiple formats. Plain text, markdown, HTML, JSON, latex for formulas. You choose based on what you need. If you're building a website, HTML makes sense. If you're storing data in a database, JSON is the way to go. If you're writing a report, markdown works great. The flexibility is huge. And because it's open- source, you can customize it. You can fine-tune it on your own data. Let's say you're in a specific industry with specialized terminology, medical, legal, technical, you can train the model on your documents and make it even better for your use case. Not many OCR tools let you do that. So, here's the bottom line.

GLMOCR is free, open source, and state-of-the-art. It handles text, tables, formulas, handwriting, and key information extraction. It's small enough to run locally, but powerful enough to compete with models 10 times its size. And it's ready to use right now. You can download it from Hugging Face, run it with Alama, integrate it into your code, deploy it with VLM or SGLANG, whatever you need. So, go try it, download it, test it on your documents, see what it can do. And if you want to learn how to build systems like this with AI tools like GMOCR, you need to join the AI profit boardroom. That's where I share the full workflows, SOPs, and automation strategies that are saving businesses hundreds of hours every month. We've got members scaling their companies with AI right now. They're automating customer service, data entry, content creation, all of it. And you can do the same. Links in the description. And if you want the full process, SOPs, and 100 plus AI use cases like this one, join the AI success lab. Links in the comments and description. You'll get all the video notes from there, plus access to our community of 38,000 members who are crushing it with AI. That's it for today. If you learned something, drop a comment below. Julian reads every single one.