DeepSeek converts text documents into compressed images to cut the token cost of storing information in large language models. The method achieves 96% text recovery at 10x compression and 60% at 20x compression, generating over 200,000 training pages daily on a single GPU.
What if the best way for an AI to remember a book is to stop reading it? You see, every word an AI reads becomes tokens. And tokens are expensive. The longer the book, the more the AI chokes on it. But we humans do something strange. We don't remember everything perfectly. Some details fade. The older grandpa is, the better he was at school. And scientists at DeepSeek asked the question, What if we make an AI do that too? Asking artificial intelligence to do artificial forgetting. But— what a crazy proposition! Why would you do that? How does that even make sense? Well, let's see together. As we keep talking to an AI system, the longer we talk, the more documents we attach, the more expensive it is to keep running, because it has to remember everything. Now, DeepSeek OCR says not to worry, let's take this big soup of information, even text, and render it as an image. And then, compress that image. A bit like what JPEG compression does to images. But this is learned and token-based. And now, this data is drastically smaller. But this still doesn't make too much sense. So, how is that good? I mean, we lose a lot of information, right? Well, hold on to your papers, Fellow Scholars, and let's see how much information we lose at 10x compression level, that is throwing away almost everything and using 90% less space. That is a lot of compression, and— what? Can that be? We can recover more than 96% of the text! That is insane! So, why not push it a bit further? Well, as we go up to nearly 20x compression, OCR accuracy falls to 60%. Wow. I expected much, much lower. Okay, now, important question. Is this really new? I mean, at first, I was a bit confused by this work. Because this is an unassuming paper about OCR. Yes, this does OCR, so you give it a document, maybe even a handwritten note, And it gives you the text back. Formally, it takes a small amount of compressed vision tokens and turns them into a much larger number of text tokens. But is this just JPEG? I mean, that technology is more than 30 years old. The idea is good, but is this just JPEG plugged into an AI system? Well, not quite. There is tons of new stuff here. Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér. You see, JPEG compresses pixels. This compresses memories. First, these memories are turned into a picture, and then we compress that picture. But we are not done yet. No, no, no. Not even close. The key engineering trick is that they created an encoder that is built to survive very high-resolution documents without exploding in cost. To do that, 1, it uses local window attention first, 2, a 16x convolutional compressor, and 3, it does global visual processing. Okay, and what does that mean? Well, in other words, it does not stare at every pixel with full attention. No! It skims, it squeezes, and only then, it reads. And here is something brilliant. Once you can do this cheaply enough, suddenly, this is not just about remembering old data. Wait, does that mean that it can not only understand documents, but also help generate new data? Yes! The paper says it can generate training data for LLMs and VLMs at more than 200,000 pages per day on a single A100 GPU. Insane speeds. Now, we are Fellow Scholars here, so we look at limitations too. The paper itself says OCR alone is not enough to prove true text compression, and a later stress test suggests the model may rely heavily on language priors, not just visual memory. In other words, when the image becomes too compressed, it may not truly see the missing text anymore, but it also guesses the most likely words from the context. Kinda like autocomplete, but better. Overall, this is absolutely fantastic. This is not only an open system that is free for all of us, but it also creates training data to help other open models come into being. That is absolutely amazing. What a time to be alive! And don't forget, every research paper pushes humanity forward. But the future belongs to the ones who understand it. Perhaps subscribe and hit the bell if you feel that you enjoyed this. I use LaMDA to reproduce AI research papers, often in minutes. It's also great to train your own models, or fine-tune an existing one. Run inference, or text to image or video? Easy peasy. Running a DeepSeek chatbot or agent? Super fast, super reliable. Lambda gives you powerful NVIDIA GPUs to run your own experiments. I test ideas from the papers I cover, and moments later, Results. Love it. Seriously, try it out now at lambda.ai/papers.
