Elastic has announced the launch of jina-ocr-v1, a new optical character recognition (OCR) model for end-to-end document processing. At 574M active parameters, it delivers frontier-grade accuracy in a model roughly one tenth the size of the benchmark leader. Jina-ocr-v1 accurately converts complex visual documents into structured, machine-readable text, such as Markdown, in a single pass, making it easy to search, train models, and build agentic applications using the data from scanned documents.
Quick Intel
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Elastic launches jina-ocr-v1, an end-to-end OCR model for document processing.
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The model has 574M active parameters and delivers frontier-grade accuracy.
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It processes complex layouts, tables, handwriting, and math across 100+ languages.
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Jina-ocr-v1 scores 83.4 on olmOCR-bench, the highest published score among models with fewer than 600M active parameters.
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It is available via the Elastic Inference Service, included with Elastic Cloud.
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Developers can also access it through the Jina API or deploy it on-premises.
While traditional OCR works well on clean text and simple layouts, complex documents with highly visual content often require separate processing steps, such as page segmentation, element classification, text recognition and reassembly. Each step introduces potential for errors that can accumulate through the fragile processing pipeline. When inaccurate or incomplete data is passed downstream, agents can return incomplete facts, and RAG pipelines can return answers that don't accurately reflect the source documents.
A Single Model for End-to-End Processing
jina-ocr-v1 handles the entire process end to end in a single model. It uses a mixture-of-experts architecture with 3.4B total parameters and 574M active at inference, running at the speed and cost of a sub-600M model. jina-ocr-v1 also adds FastMTP technology, which improves multi-token prediction to accelerate inference.
In a single model, jina-ocr-v1 can work across a broad range of imaged documents, processing images of varying quality, including scanned pages, photographed documents, slides and label images from source formats such as PDF, PPTX and XLSX. It preserves document structure by processing complex layouts and returning structured Markdown that retains headings, sections, lists and reading order. It extracts tables by converting them into basic HTML format, suitable for further processing and importing into spreadsheets or other applications. It recognizes handwriting, including block text in a wide array of languages, and English cursive. It reads more than 100 languages, understanding a wide array of global languages and scripts, with all major international languages and scripts represented. It also converts mathematical notation by transforming printed formulas into LaTeX math code for use in documents and scientific applications.
Frontier-Grade Accuracy at a Fraction of the Size
At a tenth the size of the olmOCR-bench leader, jina-ocr-v1 scores 83.4 on olmOCR-bench, the highest published score among models with fewer than 600M active parameters. It delivers frontier-grade accuracy on less hardware, outperforming frontier LLMs on character-level accuracy and reading order.
"Customers need an easy way to digitize their information more than ever in the age of AI," said Han Xiao, vice president of AI, Elastic. "Traditional OCR pipelines break down with complex layouts, tables, handwriting and other highly visual content. Until now, companies either had to accept those limitations or pay a significant premium to use general-purpose LLMs for ingesting documents. We built jina-ocr-v1 to handle that full range of complexity in a single model, while remaining very efficient at scale."
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