The world needs two types of document OCR: 1. Extremely fast/cheap OCR tools: Each organization mig...
Jerry Liu(@jerryjliu0) · 人工智能
The world needs two types of document OCR: 1. Extremely fast/cheap OCR tools: Each organization might have trillions of documents, and this is oftentimes helpful as a 'first pass' to digitalize each document so that it's accessible to AI agents. Accuracy might not be the highest, but it's 'good enough' for agentic retrieval and a deeper second pass. This includes free/OSS tools like pypdf, pymupdf. Over the past year, we've invested heavy efforts in this area through `liteparse` - the fastest/most accurate free/OSS parser. It integrates with 5 other open-weight OCR tools, most recently with MiniCPM-OCR from @OpenBMB github.com/run-llama/lite… We're also doing a lot more in this space - stay tuned! 2. Accurate VLM-based OCR: This includes the subset of documents that are a) complex, and b) the organization must parse accurately. The data needs to be accurate because this data is used in business critical workflows across finance, insurance, healthcare, and more. At the same time, even if the tool is not free, any VLM needs to be able to scale to billions of pages in a cost-effective manner. Two weeks ago we launched OCRia-1, the best general purpose model for document parsing. It not only parsers complex text layouts, complex tables, but also visual diagrams and charts. llamaindex.ai/blog/parse-ocr… It powers all our modes within LlamaParse; see the most recent benchmarks on ParseBench: parsebench.ai ------- Both of these document OCR types require rigorous benchmarks, data+training pipelines, and an ambitious/talent-dense team pushing the pareto frontier of accuracy and cost. If you're looking to solve document understanding across either frontier, come talk to us! 💬 10 🔄 2 ❤️ 21 👀 1422 📊 17 ⚡ Powered by xgo.ing