Elastic Introduces jina-ocr-v1: End-to-End Document Processing in a Single Frontier-Grade Model

SAN FRANCISCO--( BUSINESS WIRE)-- Elastic (NYSE: ESTC) today 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.

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.

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:

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.”

Availability

jina-ocr-v1 is available now via the Elastic Inference Service, included with Elastic Cloud, with preconfigured model provisioning and GPU acceleration. Developers can access the model through a preconfigured endpoint without hosting the model or provisioning their own GPUs.

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